Recently, location-based routings in wireless sensor networks (WSNs) are attracting a lot of interest in the research community, especially because of its scalability. In location-based routing, the network size is scalable without increasing the signalling overhead as routing decisions are inherently localized. Here, each node is aware of its position in the network through some positioning device like GPS and uses this information in the routing mechanism. In this paper, we first discuss the basics of WSNs including the architecture of the network, energy consumption for the components of a typical sensor node, and draw a detailed picture of classification of location-based routing protocols. Then, we present a systematic and comprehensive taxonomy of location-based routing protocols, mostly for sensor networks. All the schemes are subsequently discussed in depth. Finally, we conclude the paper with some insights on potential research directions for location-based routing in WSNs.
In recent years, location-based routing has emerged as the prominent area of research in the field of wireless sensor networks (WSNs). Sensor nodes may not have the internet protocol (IP) addresses; therefore, IP-based protocol cannot be used for the sensor networks. Building an efficient, scalable and simple protocol for WSN is very challenging due to limited resources and the dynamic nature of sensor networks. In location-based routing, the node does not need to make complex computations to find the next hop, as routing decisions are taken using the location information. Location-based protocols are very efficient in terms of routing data packet as they take the advantage of pure location information instead of global topology information. This paper presents a survey and taxonomy of location-based routing for sensor networks.
In location-based routing, a node that has a packet to send (Source) adds a destination location (Sink) in each data packet. Intermediate nodes in the path receive this packet and send it to next one-hop neighbours which are geographically closest to the destination. The process is continued until the data packets are received by the destination node. In location-based routing, the state required to be maintained in each node is minimum, because of the locality. It has low communication overhead because advertisements of routing tables, like in traditional routing protocols, are not needed. Location-based routing thus does not require the establishment or maintenance of routes. Therefore, Location-based routing conserves both energy and bandwidth since route request and state propagation are not required after one-hop distance.
Location-based routing uses the location information for nodes to provide higher efficiency and scalability. It requires three facts: first, each node in the network must know its own location information by GPS or by any other methods [1] [2] [3] [4] . Second, each node must be aware of its neighbour node’s location which is one-hop away from it. Third, the source must be aware of the location of destination node.
Location-based routing requires the correct location information, which can be obtained using some localization mechanism. Location information is essential for many wireless network applications, so it is expected that each wireless sensor node in the network will be equipped with some localization devices. Several techniques [1] [3] [4] exist for location sensing based on proximity or triangulation using radio signals, acoustic signals, or infrared. These techniques differ in their localization granularity, range, deployment complexity and cost. Most of the location mechanisms utilize the flooding [5] to spread the sink’s location up to all other nodes, which is undesirable for the large-scale WSNs, especially when the multiple mobile sinks and sources exist.
Location-based routing usually uses a greedy forwarding mechanism to forward a data packet from source to destination. Greedy approach forwards packets to the neighbour, which is closest to the destination. It assumes that the network is sufficiently dense; each node has its own accurate location information, its neighbours’ locations, and high link reliability. Dense sensor deployment [6] and reasonably accurate location information may be acceptable in some WSN applications. But the high link reliability is not acceptable in any realistic deployment. Because recent studies [7] [8] show that wireless links can be highly unreliable and have to deal with the higher-layer protocols.
Many forwarding strategies [9] are proposed to improve the performance of geographic routing. These forwarding strategies can be broadly divided into two categories: distance-based and reception-based. While the nodes need to know only the distance to their neighbours in distance-based strategy, the nodes also know the packet reception rates of their neighbours in reception-based strategy. Both strategies use the greedy-like forwarding to choose the next hop in the forwarding process.
In order to understand localtion based routing in WSN, we begin with an overview on WSN. The overview includes common architectures of sensor networks, available protocols and application areas in WSN. We then provide a taxonomy of localtion-based routing protocols for WSN. A table comparing different location-based routing protocols is given at the end of this paper. In addition, we discuss the available ad-hoc routings in WSN. To the best of our knowledge, there is no paper to give a survey on location-based routing for WSNs. With this article, readers can have a more thorough and delicate understanding of location-based routing for WSNs and the research trend in this area.
2. Wireless Sensor Networks2.1. Architecture of WSNs
WSNs have attracted many researchers in this field of research in the past few years. The advancement in the field of micro electro mechanical systems (MEMS) has open the way to develop low-cost, low-power, multi-functional, tiny sensor nodes [10] [11] [12] . These tiny nodes are capable to sense the environment, perform data processing and having the capability to communicate with other nodes in the network over short distances. A typical architecture of WSN is shown in Figure 1.
WSN consists of at least one sink node (or base station) and a (large) number of sensor nodes deployed in the network field. Sensor nodes in the field sense and collect raw data from the environment to do some local processing, communicate with each other to perform aggregation in necessary, and then route the aggregated data to the sink. Sink or base station serves as a destination node for the sensor nodes. User can access the data from the sink by internet or satellite.
Architecture of wireless sensor network
A large-scale WSN consists of thousands of sensor nodes deployed according to the application [13] [14] . Wireless sensor nodes are small devices that have three basic components. First, a sensing device for data acquisition from the physical surrounding environment. Second, a processing unit for local data processing and storage. Third, a wireless communication device for data transmission. An archi- tecture of typical wireless sensor node [15] [16] and a berkeley Mote (Real-life sensor node) [17] is shown in Figure 2 and Figure 3 respectively.
These sensor nodes have several resource constraints such as limited memory, battery power, signal processing, computation and communication capabilities. These nodes are mainly deployed in remote areas and intended to work for WSN research interest. Many research prototype sensor nodes such as UCB motes [18] , uAMPS [19] [20] , MICA [21] and PC104 [22] have been designed many years so lifetime extension of sensor networks play an important role in and manufactured. Energy efficient MAC [23] [24] [25] , and different routing schemes are implemented and evaluated for the real life application of WSNs.
WSNs require sensor nodes to communicate with each other frequently depending on the application, making data dissemination a challenging task in large networks. During data dissemination in WSNs, data transmission consumes more energy than data processing in a sensor node. The energy required to transmit a single bit is comparable to the amount required to process a few
thousands operations in a typical sensor node [26] . Sensing subsystem energy consumption depends on the sensor type. In some cases, it is negligible when compared with processing and transmission energy. But on the other hand, data sensing may be comparable to, or even greater than, the energy needed for data transmission.
Mainly, energy-saving techniques focus on network subsystem and sensing subsystem. In networking subsystem, energy management is taken into account in the operations of each single node, as well as in the design of networking protocols. In sensing subsystem, techniques are used to reduce the amount or frequency of energy-expensive samples. The lifetime of the sensor network can be prolonged by applying different energy conservation techniques. However, the main energy consumption components are CPU radio, even if they are idle. Therefore, different power management schemes are used to switch off the node components that are temporarily not necessary. Conserving the energy of the nodes can prolong the lifetime of the whole network.
Sensor networks can be broadly classified into two types; homogeneous and heterogeneous sensor networks. In homogeneous sensor network, all the sensor nodes are identical in terms of battery energy and hardware complexity. On the other hand, two or more different types of nodes with different battery energy and functionality are used in heterogeneous sensor network.
Sensor nodes do not have the IP address so, the queries are directed to a region containing a cluster of sensors rather than specific sensor addresses. We can say that sensor networks are predominantly data-centric rather than address-centric. Aggregation of the data is performed locally and a summary or analysis of the local data is prepared by an aggregator node within the cluster, thus reducing the communication bandwidth requirements. Dedicated routing protocol is required for dissemination of data packet through shortest path. Redundancy must be considered to avoid congestion when different nodes are sending and receiving the same information. However, redundancy must be exploited to ensure network reliability. Data dissemination can be query driven or based on continuous updates.
2.2. MAC for WSNs
In past few years, several medium access control (MAC) layer protocols were proposed to improve the performance of the sensor network. MAC protocols basically prolong the network life time by switching on and off the sensor node components like the radio transceiver. B-MAC [27] is a low power and flexible MAC protocol based on CSMA. It provides basic operations and interfaces help dynamically change the parameter of the protocol to compromise with the varying communication environment. B-MAC also provides two-directional interface for routing protocol and MAC protocol to communicate with each other by calibrating the parameter of the interface. It utilizes the sleep and wakeup technique to save the energy of the node. It does not support multicast and thus messages have to wait for another cycle to forward the data.
RMAC is an energy efficiency duty cycle MAC protocol for WSN. RMAC uses sleep period to send/receive data between nodes and forward control packet along the route during one duty cycle. The control packet helps the receiver to calculate the exact wakeup time. It uses data period to transmit PION packet instead of data and calculates to forward data packet to more than one node along the route in sleep mode. It does not support broadcast mode and bust data mode.
Z-MAC is a hybrid adaptive MAC protocol. The main idea is to make of CSMA as the foundation and TDMA as the hint for protocol. Based on the B-MAC operation, Z-MAC proposed a flexible and adaptive MAC with the data in the network. Z-MAC easily adapt with the changing content of the sensor network. It is suitable for the application with medium to high, two-hop contention. The drawback of Z-MAC is that many algorithms were needed to initialize and maintain the status of the network such as DRAND to assign node slot, TPSN to synchronize the global clock.
T-MAC is a contention-based MAC protocol for WSN which uses the duty cycle (alternatively active/sleep node) to increase the life of the nodes. It proposed an adaptive duty cycle by dynamically ending the active part of it. This can reduce the energy consumption on idle listening, while giving a constant and reasonable throughput.
S-MAC [28] utilized three novel techniques to reduce energy consumption and support self-configuration. Nodes periodically sleep to save the energy wasted by idle listening. Neighbouring nodes form virtual clusters auto-syn- chronize on sleep schedules. S-MAC utilizes the message passing technique to reduce contention latency for sensor-network applications that require store- and-forward processing as data moves through the network. S-MAC has better energy conserving properties compared to the IEEE 802.11. It also has the ability to trade-off between energy and latency according to traffic conditions.
2.3. Application of WSNs
A wireless sensor network is a wireless network consisting of spatially distributed autonomous devices with sensing, computation and wireless communication capabilities to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants at different locations. The development of the sensor network was originally motivated by military applications such as enemy tracking, tank and soldier surveillance and battle field surveillance. Today we can see a large number of application of WSNs, like industrial process monitoring and control [29] [30] , environment observation and habitat monitoring [31] [32] [33] , healthcare applications [34] [35] [36] , home automation [37] [38] [39] , traffic control [40] [41] and forecast systems [42] [43] [44] .
WSN applications typically involve the surveillance of physical phenomenon through the sampling of the environment situation. Accordingly, WSN applications can be classified as tracking and monitoring categories. Overall classification of sensor network applications is depicted in Figure 4.
Location-based routing plays a massive role in the military application, volcano monitoring, environment observation, traffic control, forecast system and so on. Location-based routing protocols are efficient for getting the information from a particular region. It is also very useful when the terrain is not accessible by human being like volcano monitoring. Many real life projects are running in the different parts of the world using the location-based routing in to consideration like Zebranet [45] , wildCENSE [46] , PinPtr [47] , CenWits [48] and volcanic monitoring [49] .
3. Ad-Hoc Routings
Many ad-hoc routing protocols were proposed to enhance the efficiency of ad-hoc network. Most well-known protocols are like DSR [50] , DSDV [51] , TORA [52] and AODV [53] . Both of Dynamic source routing (DSR) [50] and Ad-hoc on demand distance vector routing (AODV) [53] protocols flood the route request on-demand which help to save the bandwidth and also increase the battery power (not sending and receiving the message unnecessarily). Even if the link is broken, the AODV provides loop-free routes. On the other hand, temporally- ordered routing algorithm (TORA) [52] is from the family of “link reversal” algorithms. TORA is best to use in large, dense and mobile networks. TORA is an efficient and scalable protocol and shows a high degree of adaptively. Sensor networks require more energy and bandwidth saving than the Ad-hoc networks; and the communication in sensor networks is data-centric as opposed to address- centric in ad-hoc networks. And the communication in WSN is data-centric as opposed to address-centric in ad-hoc networks. Therefore the ad-hoc network
Overview of sensor network applications
protocols are undesirable for sensor networks.
The few major differences between wireless sensor networks and ad-hoc networks are given below.
1) Sensor networks are mainly used to collect information while MANETs (Mobile Ad-hoc Networks) are designed for distributed computing rather than information gathering.
2) The number of sensor nodes in a sensor network can be several orders of magnitude higher than the nodes in an ad hoc network.
3) Sensor nodes are densely deployed.
4) Sensor nodes are prone to failures.
5) The topology of a sensor network changes very frequently.
6) Sensor nodes mainly use a broadcast communication paradigm, whereas most ad hoc networks use point-to-point communications.
7) Sensor nodes are limited in power, computational capacities and memory.
8) Unlike a node in ad-hoc networks, a node in sensor network may not have global Identification (ID) because of the large amount of overhead and large number of sensors.
9) Sensor nodes are much cheaper than nodes in ad hoc networks.
10) Usually, the data in sensor networks are bound either downstream to nodes from a sink or upstream to a sink from nodes, while in MANETs, the data flows are irregular.
4. Location-Based Protocols for WSNs
In this section, we survey the state-of-the-art location-based routing protocols for WSNs. Sensor nodes may not have the internet protocol (IP) addresses, therefore IP-based protocols cannot be used for the sensor networks. Building an efficient, scalable and simple protocol for WSN is very challenging due to limited resources and the dynamics nature of sensor network. In location-based routing, the node do not need to make complex computations to find the next hop, as routing decisions are taken using the location information. Location- based protocols are very efficient in terms of routing data packet as they take the advantage of pure location information instead of global topology information.
Location-based protocol uses the location information of nodes to provide higher efficiency and scalability. It requires three facts. First, each node in the network must know its own location information by GPS or by any other methods [1] [2] [3] . Second, each node must be aware of its neighbour nodes’ location, which are one-hop away from it. Third, the source node must be aware of the location of destination node. Location-based routing protocols can be mainly categories as shown in Figure 5.
Most of the location-based protocols are using the greedy algorithms to forward the packets to the destination. These algorithms only differ in how they handle the hole communication problem.
The earliest work in location-based is done by Finn and Gregory G. [54] in 1987. In this report, they proposed a flat routing mechanism called Cartesian
Categorization of location-based routing protocols
routing. The drawback of the mechanism was its difficulty in determining an appropriate scope for the search.
Greedy perimeter stateless routing (GPSR) [55] uses a planar graph to avoid this problem. Planar graph is derived from the original network graph. The packet follows the perimeter of the planar graph to circumvent holes. The de- rived planar graph is much sparser than the original one, and the traffic concen- trates on the perimeter of the planar graph in perimeter mode. Thus, the nodes on the planar graph tend to be depleted quickly. In addition, nodes are assumed to operate in promiscuous listening mode and consequently consume energy.
Location aided routing (LAR) [56] , was proposed for the ad-hoc network. It uses the location information (location information obtained by GPS) to find the new route. By using the location information, the LAR limits the search in a smaller region called “request zone”. Limiting the search in the request zone significantly reduces the number of search message. The request zone is estimated by the previous information of the location and mobility pattern of the nodes. In case, the mobility pattern is not accurate, the request zone can be extended up to the whole network field.
The categorization of location-based routing is shown in Figure 5. To present the protocols in this paper we have divided the protocols in two sections, ac- cording to mobile or static nodes present in the network. Each protocol in these sections states which method they use to acquire the location information. In both sections, we survey in detail the protocols that fall below the location-based routing in WSN. Table 1 shows the detailed comparison among those location-based routing algorithms mentioned here. The properties chosen for comparison are mobility, energy-awareness, self-reconfiguration, negotiation, data aggregation, quality of service (QoS), state complexity, scalability, multipath, query-based, and GPS incorporation. Routing algorithms considering mobile nodes in the network are complex in nature as opposed to the static nodes. Due to limited energy of the tiny wireless sensor nodes, energy- awareness is an important characteristic for location-based routing protocols in WSN. As these protocols requires the location information, wireless sensor
ReferencesSavvides, A., Han, C.C. and Strivastava, M.B. (2001) Dynamic Fine-Grained Localization in Ad-Hoc Networks of Sensors. Proceedings of the 7th Annual International Conference on Mobile Computing and Networking (MOBICOM’01), Rome, 16-21 July 2001, 166-179. https://doi.org/10.1145/381677.381693Savvides, A., Park, H. and Srivastava, M.B. (2003) The n-Hop Multilateration Primitive for Node Localization Problems. Mobile Networks and Applications, 8, 443-451. https://doi.org/10.1023/A:1024544032357Savvides, A. and Srivastava, M.B. (2004) Location Discovery. In: Basagni, S., Conti, M., Giordano, S. and Stojmenovic, I., Eds., Mobile Ad Hoc Networking, John Wiley & Sons, Inc., Hoboken. https://doi.org/10.1002/0471656895.ch8Niculescu, D. (2004) Positioning in Ad Hoc Sensor Networks. IEEE Network, 18, 24-29. https://doi.org/10.1109/MNET.2004.1316758Intanagonwiwat, C., Govindan, R. and Estrin, D. (2000) Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks. Proceedings of the 6th Annual International Conference on Mobile Computing and Networking, Boston, 6-11 August 2000, 56-67. https://doi.org/10.1145/345910.345920Srinivasan, K., Dutta, P., Tavakoli, A. and Levis, P. (2006) Understanding the Causes of Packet Delivery Success and Failure in Dense Wireless Sensor Networks. Proceedings of the 4th International Conference on Embedded Networked Sensor Systems (SenSys’06), Boulder, 31 October-3 November 2006, 419-420.
https://doi.org/10.1145/1182807.1182885Woo, A., Tong, T. and Culler, D. (2003) Taming the Underlying Challenges of Reliable Multihop Routing in Sensor Networks. Proceedings of the 1st International Conference on Embedded Networked Sensor Systems (SenSys’03), Los Angeles, 5-7 November 2003, 14-27. https://doi.org/10.1145/958491.958494Zhao, J. and Govindan, R. (2003) Understanding Packet Delivery Performance in Dense Wireless Sensor. Proceedings of the 1st International Conference on Embedded Networked Sensor Systems (SenSys’03), Los Angeles, 5-7 November 2003, 1-13. https://doi.org/10.1145/958491.958493Seada, K., Zuniga, M., Helmy, A. and Krishnamachari, B. (2004) Energy-Efficient Forwarding Strategies for Geographic Routing in Lossy Wireless Sensor Networks. Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems, Baltimore, 3-5 November 2004, 108-121.
https://doi.org/10.1145/1031495.1031509Yick, J., Mukherjee, B. and Ghosal, D. (2008) Wireless Sensor Network Survey. Computer Networks, 52, 2292-2330. https://doi.org/10.1016/j.comnet.2008.04.002Akkaya, K. and Younis, M. (2005) A Survey on Routing Protocols for Wireless Sensor Networks. Ad Hoc Networks, 3, 325-349.
https://doi.org/10.1016/j.adhoc.2003.09.010Akyildiz, I.F., Su, W., Sankarasubramaniam, Y. and Cayirci, E. (2002) Wireless Sensor Networks: A Survey. Computer Networks, 38, 393-422.
https://doi.org/10.1016/S1389-1286(01)00302-4Nasipuri, A., Cox, R., Conrad, J., Van der Zel, L., Rodriguez, B. and McKosky, R. (2010) Design Considerations for A Large-scale Wireless Sensor Network for Substation Monitoring. IEEE 35th Conference on Local Computer Networks (LCN’10), Denver, 10-14 October 2010, 866-873.Hur, S., Kim, J., Choi, J. and Park, Y. (2008) An Efficient Addressing Scheme and Its Routing Algorithm for a Large-Scale Wireless Sensor Network. EURASIP Journal on Wireless Communications and Networking, 2008, Article ID: 765803.
https://doi.org/10.1155/2008/765803Vieira, M.A.M., Coelho, C.N., da Silva, D.C. and da Mata, J.M. (2003) Survey on Wireless Sensor Network Devices. Proceedings of IEEE Conference on Emerging Technologies and Factory Automation (ETFA’03), 1, 537-544.
https://doi.org/10.1109/etfa.2003.1247753Zhu, Z.H. and Yang, S.-H. (2006) A Possible Hardware Architecture of Wireless Sensor Nodes. Proceeding of IEEE Conference on Systems, Man, and Cybernetics, Taipei, 8-11 October 2006, 3377-3381. https://doi.org/10.1109/icsmc.2006.384640http://siliconrobot.com/macro_motes/macromotes.htmlHill, J. and Cullar, D. (2002) A Wireless Embedded Sensor Architecture for System-Level Optimization. Technical Report, University of California at Berkeley.Calhoun, B.H., Daly, D.C., Verma, N., Finchelstein, D.F., Wentzloff, D.D., Wang, A., Cho, S.-H. and Chandrakasan, A.P. (2005) Design Considerations for Ultra-Low Energy Wireless Microsensor Nodes. IEEE Transactions on Computers, 54, 727-740. https://doi.org/10.1109/TC.2005.98http://www-mtl.mit.edu/researchgroups/icsystems/uamps/Hill, J.L and Culler, D.E. (2002) Mica: A Wireless Platform for Deeply Embedded Networks. Proceedings of the 35th Annual International Symposium on Microarchitecture, 22, 12-24. https://doi.org/10.1109/mm.2002.1134340http://www.isi.edu/scadds/pc104testbed/guideline.htmlRhee, I., Warrier, A., Aia, M., Min, J. and Sichitiu, M.L. (2008) Z-MAC: A Hybrid MAC for Wireless Sensor Networks. IEEE/ACM Transactions on Networking, 16, 511-524. https://doi.org/10.1109/TNET.2007.900704van Dam, T. and Langendoen, K. (2003) An Adaptive Energy-Efficient MAC Protocol for Wireless Sensor Networks. Proceedings of the 1st International Conference on Embedded Networked Sensor Systems, Los Angeles, 5-7 November 2003, 171-180. https://doi.org/10.1145/958491.958512Du, S., Saha, A.K. and Johnson, D.B. (2007) RMAC: A Routing-Enhanced Duty-Cycle MAC Protocol for Wireless Sensor Networks. Proceeding of the 26th IEEE International Conference on Computer Communications (INFOCOM’07), Anchorage, 6-12 May 2007, 1478-1486. https://doi.org/10.1109/infcom.2007.174Anastasi, G., Conti, M., Di Francesco, M. and Passarella, A. (2009) Energy Conservation in Wireless Sensor Networks: A Survey. Ad Hoc Networks, 7, 537-568.
https://doi.org/10.1016/j.adhoc.2008.06.003Joseph, P., Jason, H. and David, C. (2004) Versatile Low Power Media Access for Wireless Sensor Networks. Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems, Baltimore, 3-5 November 2004, 95-107.Ye, W., Heidemann, J. and Estrin, D. (2002) An Energy-Efficient MAC Protocol for Wireless Sensor Networks. Proceedings of the IEEE 21st Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM’02), 3, 1567-1576. https://doi.org/10.1109/infcom.2002.1019408Peng, W.-C., Ko, Y.-Z. and Lee, W.-C. (2006) On Mining Moving Patterns for Object Tracking Sensor Networks. Proceeding of the 7th International Conference on Mobile Data Management (MDM’06), Nara, 9-13 May 2006, 41.
https://doi.org/10.1109/MDM.2006.114Zhang, W. and Cao, G. (2004) DCTC: Dynamic Convoy Tree-Based Collaboration for Target Tracking in Sensor Networks. IEEE Transactions on Wireless Communications, 3, 1689-1701. https://doi.org/10.1109/TWC.2004.833443Cerpa, A., Elson, J., Estrin, D., Girod, L., Hamilton, M. and Zhao, J. (2001) Habitat Monitoring: Application Driver for Wireless Communications Technology. Proceeding of ACM Workshop on Data Communication in Latin America and the Caribbean, 31, 20-41. https://doi.org/10.1145/844193.844196Chen, C.-C. and Liao, C.-H. (2011) Model-Based Object Tracking in Wireless Sensor Networks. Wireless Networks, 17, 549-565.
https://doi.org/10.1007/s11276-010-0296-5Chen, T.-S., Liao, W.-H., Huang, M.-D. and Tsai, H.-W. (2005) Dynamic Object Tracking in Wireless Sensor Networks. Proceeding of the 13th IEEE International Conference on Networks (ICON’05), Kuala Lumpur, Malaysia, 16-18 November 2005, 475-480.Milenkovic, A., Otto, C. and Jovanov, E. (2006) Wireless Sensor Networks for Personal Health Monitoring: Issues and An Implementation. Computer Communications, 29, 2521-2533. https://doi.org/10.1016/j.comcom.2006.02.011Alemdar, H. and Ersoy, C. (2010) Wireless Sensor Networks for Healthcare: A Survey. Computer Networks, 54, 2688-2710.
https://doi.org/10.1016/j.comnet.2010.05.003Kulkarni, P. and Ozturk, Y. (2011) mPHASiS: Mobile Patient Healthcare and Sensor Information System. Journal of Network and Computer Applications, 34, 402-417. https://doi.org/10.1016/j.jnca.2010.03.030Ding, D., Cooper, R.A., Pasquina, P.F. and Fici-Pasquina, L. (2011) Sensor Technology for Smart Homes. Maturitas, 69, 131-136.
https://doi.org/10.1016/j.maturitas.2011.03.016Boers, I.M., Chodos, D., Gburzynski, P., Guirguis, L., Huang, J., Lederer, R., Liu, L., Sadowski, C. and Stroulia, E. (2011) The Smart Condo Project: Services for Independent Living. E-Health, Assistive Technologies and Applications for Assisted Living: Challenges and Solutions. IGI Global, 289-314.
https://doi.org/10.4018/978-1-60960-469-1.ch013Han, D.-M. and Lim, J.-H. (2010) Smart Home Energy Management System Using IEEE 802.15.4 and Zigbee. Proceeding of IEEE Transactions on Consumer Electronics, 56, 1403-1410. https://doi.org/10.1109/TCE.2010.5606276Xu, Y. and Lee, W.-C. (2007) Compressing Moving Object Trajectory in Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 3, 151-174.
https://doi.org/10.1080/15501320701204756Chen, W., Chen, L., Chen, Z. and Tu, S. (2005) A Realtime Dynamic Traffic Control System Based on Wireless Sensor Network. Proceeding of the International Conference on Parallel Processing (ICPP’05), 14-17 June 2005, 258-264.He, T., Krishnamurthy, S., Luo, L., Yan, T., Gu, L., Stoleru, R., Zhou, G., Cao, Q., Vicaire, P., Stankovic, J.A., Abdelzaher, T.F., Hui, J. and Krogh, B. (2006) VigilNet: An Integrated Sensor Network System for Energy-Efficient Surveillance. ACM Transactions on Sensor Networks, 2, 1-38. https://doi.org/10.1145/1138127.1138128Selavo, L., Wood, A., Cao, Q., Sookoor, T., Liu, H., Srinivasan, A., Wu, Y., Kang, W., Stankovic, J., Young, D. and Porter, J. (2007) LUSTER: Wireless Sensor Network for Environmental Research. Proceedings of the 5th International Conference on Embedded Networked Sensor Systems, Sydney, 6-9 November 2007, 103-116.
https://doi.org/10.1145/1322263.1322274Xu, N., Rangwala, S., Chintalapudi, K.K., Ganesan, D., Broad, A., Govindan, R. and Estrin, D. (2004) A Wireless Sensor Network for Structural Monitoring. Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems, Baltimore, 3-5 November 2004, 13-24. https://doi.org/10.1145/1031495.1031498Zhang, P., Sadler, C.M., Lyon, S.A. and Martonosi, M. (2004) Hardware Design Experiences in ZebraNet. Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems, Baltimore, 3-5 November 2004, 227-238.
https://doi.org/10.1145/1031495.1031522http://wildcense.sourceforge.net/Simon, G., Balogh, G., Pap, G., Maróti, M., Kusy, B., Sallai, J., Lédeczi, á., Nádas, A. and Frampton, K. (2004) Sensor Network-Based Countersniper System. Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems (SenSys’04), Baltimore, 3-5 November 2004, 1-12.
https://doi.org/10.1145/1031495.1031497Huang, J.-H., Amjad, S. and Mishra, S. (2005) CenWits: A Sensor-Based Loosely Coupled Search and Rescue System Using Witnesses. Proceedings of the 3rd International Conference on Embedded Networked Sensor Systems, San Diego, 2-4 November 2005, 180-191. https://doi.org/10.1145/1098918.1098938Werner-Allen, G., Lorincz, K., Ruiz, M., Marcillo, O., Johnson, J., Lees, J. and Welsh, M. (2006) Deploying a Wireless Sensor Network on an Active Volcano. IEEE Internet Computing, 10, 18-25. https://doi.org/10.1109/MIC.2006.26Johnson, D.B. and Maltz, D.A. (1996) Dynamic Source Routing in Ad-Hoc Wireless Networks. In: Imielinski, T. and Korth, H., Eds., Mobile Computing, Kluwer Academic Publishers, 153-181. https://doi.org/10.1007/978-0-585-29603-6_5Perkins, C.E. and Bhagwat, P. (1994) Highly Dynamic Destination-Sequenced Distance-Vector routing (DSDV) for Mobile Computers. ACM SIGCOMM Computer Communication Review, 24, 234-244. https://doi.org/10.1145/190809.190336Park, V.D. and Corson, M.S. (1997) Highly Adaptive Distributed Routing Algorithm for Mobile Wireless Networks. Proceedings of the 16th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM’97), Kobe, 9 -11 April 1997, 1405-1413.Perkins, C.E. and Royer, E.M. (1999) Ad-Hoc On-Demand Distance Vector Routing. Proceedings of the 2nd IEEE Workshop on Mobile Computing Systems and Applications (WMCSA’99), New Orleans, 25-26 February 1999, 90-100.
https://doi.org/10.1109/MCSA.1999.749281Finn, G.G. (1987) Routing and Addressing Problems in Large Metropolitan Scale Internetworks. USC/ISI Technical Report ISI/RR, March 1987, 87-180.Karp, B. and Kung, H.T. (2000) GPSR: Greedy Perimeter Stateless Routing for Wireless Networks. Proceedings of the 6th Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom), Boston, 6-11 August 2000, 243-254. https://doi.org/10.1145/345910.345953Ko, Y.-B. and Vaidya, N.H. (1998) Location-Aided Routing (LAR) in Mobile Ad-Hoc Networks. Proceedings of the 4th Annual ACM/IEEE International Conference on Mobile Computing and Networking, Dallas, Texas, United States, 25-30 October 1998, 66-75. https://doi.org/10.1145/288235.288252Rodoplu, V. and Meng, T.H. (1999) Minimum Energy Mobile Wireless Networks. IEEE Journal on Selected Areas in Communications, 17, 1333-1344.
https://doi.org/10.1109/49.779917Li, L. and Halpern, J.Y. (2001) Minimum-Energy Mobile Wireless Networks Revisited. Proceeding of IEEE International Conference on Communications (ICC’01), Helsinki, 11-14 June 2001, 278-283.Xu, Y., Heidemann, J. and Estrin, D. (2001) Geography-Informed Energy Conservation for Ad Hoc Routing. Proceedings of the 7th Annual International Conference on Mobile Computing and Networking, Rome, Italy, 16-21 July 2001, 70-84.
https://doi.org/10.1145/381677.381685Yu, Y., Estrin, D. and Govindan, R. (2001) Geographical and Energy Aware Routing: A Recursive Data Dissemination Protocol for Wireless Sensor Networks. UCLA Computer Science Department Technical Report.Kim, K., Yun, J., Yun, J., Lee, B. and Han, K. (2009) A Location Based Routing Protocol in Mobile Sensor Networks. Proceedings of the 11th International Conference on Advanced Communication Technology (ICACT’09), Phoenix Park, 15-18 February 2009, 1342-1345.Shen, H. and Zhao, L. (2013) ALERT: An Anonymous Location-Based Efficient Routing Protocol in MANETs. IEEE Transactions on Mobile Computing, 12, 1079-1093. https://doi.org/10.1109/TMC.2012.65Yu, Q., Zhang, B., Liu, C. and Mouftah, H.T. (2008) Energy-Efficient Geographical Forwarding Algorithm for Wireless Ad Hoc and Sensor Networks. Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC’08), Las Vegas, 31 March-3 April 2008, 2468-2473. https://doi.org/10.1109/wcnc.2008.434Luo, D. and Zhou, J. (2011) An Improved Hybrid Location-Based Routing Protocol for Ad Hoc Networks. Proceedings of the 7th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), Wuhan, 23-25 September, 1-4. https://doi.org/10.1109/wicom.2011.6040306Sammut, E. and Debono, C.J. (2015) A Location-Based Routing Algorithm for Wireless Sensor Networks. Proceedings of IEEE International Conference on Computer as a Tool (EUROCON’15), Salamanca, 8-11 September 2015, 1-5.
https://doi.org/10.1109/eurocon.2015.7313687Sun, M.-T., Sakai, K., Hamilton, B.R., Ku, W.-S. and Ma, X. (2011) G-STAR: Geometric STAteless Routing for 3-D Wireless Sensor Networks. Ad Hoc Networks, 9, 341-354. https://doi.org/10.1016/j.adhoc.2010.07.013Malwe, S.R., Rohilla, S. and Biswas, G.P. (2016) Location and Selective-Bordercast Based Enhancement of Zone Routing Protocol. 3rd International Conference on Recent Advances in Information Technology (RAIT), Dhanbad, 3-5 March 2016, 83-88. https://doi.org/10.1109/rait.2016.7507880Yuksel, M., Pradhan, R. and Kalyanaraman, S. (2002) TBF: Trajectory-Based Forwarding Mechanisms for Ad-HOC Networks. Wireless Networks, 8, 481-494.Xing, G., Lu, C., Pless, R. and Huang, Q. (2004) On Greedy Geographic Routing Algorithms in Sensing-Covered Networks. Proceedings of the 5th ACM International Symposium on Mobile Ad Hoc Networking and Computing, Tokyo, 24-26 May 2004, 31-42. https://doi.org/10.1145/989459.989465Zorzi, M. and Rao, R. (2003) Geographic Random Forwarding (GeRaF) for Ad Hoc and Sensor Networks: Multihop Performance. IEEE Transaction on Mobile Computing, 2, 337-348. https://doi.org/10.1109/TMC.2003.1255648Zorzi, M. and Rao, R. (2003) Geographic Random Forwarding (GeRaF) for Ad Hoc and Sensor Networks: Energy and Latency Performance. IEEE Transactions on Mobile Computing, 2, 349-365. https://doi.org/10.1109/TMC.2003.1255650Casari, P., Marcucci, A., Nati, M., Petrioli, C. and Zorzi, M. (2005) A Detailed Simulation Study of Geographic Random Forwarding (GERAF) in Wireless Sensor Networks. Proceedings of the IEEE Military Communications Conference (MIL-COM’05), Atlantic City, 17-20 October 2005, 59-68.Zhang, R., Zhao, H. and Labrador, M.A. (2006) The Anchor Location Service (ALS) Protocol for Large-Scale Wireless Sensor Networks. Proceedings of the 1st International Conference on Integrated Internet Ad-Hoc and Sensor Networks, Nice, 30-31 May, 2006, Article No. 18. https://doi.org/10.1145/1142680.1142704Fan, Y. (2002) TTDD Simulation. Unpublished Work, 26 February 2002.Ye, F., Luo, H., Cheng, J., Lu, S. and Zhang, L. (2002) A Two-Tier Data Dissemination Model for Large-Scale Wireless Sensor Networks. Proceedings of the 8th Annual International Conference on Mobile Computing and Networking (MOBI-COM’02), Atlanta, 23-28 September, 2002, 148-159.Luo, H., Ye, F., Cheng, J., Lu, S. and Zhang, L. (2005) TTDD: Two-Tier Data Dissemination in Large-Scale Wireless Sensor Networks. Wireless Networks, 11, 161-175. https://doi.org/10.1007/s11276-004-4753-xIntanagonwiwat, C., Govindan, R., Estrin, D., Heidemann, J. and Silva, F. (2003) Directed Diffusion for Wireless Sensor Networking. IEEE/ACM Transaction on Networking, 11, 2-16. https://doi.org/10.1109/TNET.2002.808417Zhang, H. and Shen, H. (2007) EEGR: Energy-Efficient Geographic Routing in Wireless Sensor Networks. Proceedings of the 36th International Conference on Parallel Processing (ICPP’07), Xi’an, 10-14 September 2007, 67.
https://doi.org/10.1109/icpp.2007.37Takagi, H. and Kleinrock, L. (1984) Optimal Transmission Ranges for Randomly Distributed Packet Radio Terminals. IEEE Transactions on Communications, 32, 246-257. https://doi.org/10.1109/TCOM.1984.1096061Stojmenovic, I. and Lin, X. (2001) Power-Aware Localized Routing in Wireless Networks. IEEE Transactions on Parallel and Distributed Systems, 12, 1122-1133.
https://doi.org/10.1109/71.969123Wu, S. and Candan, K.S. (2004) GPER: Geographic Power Efficient Routing in Sensor Networks. Proceedings of the 12th International Conference on Network Protocols (ICNP’04), Berlin, 8 October 2004, 161-172.Odorizzi, A. and Mazzini, G. (2007) M-GERAF: A Reliable Random Forwarding Geographic Routing Protocol in Multisink Ad-Hoc and Sensor Networks. International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS’07), Xiamen, 28 November-1 December 2007, 416-419.
https://doi.org/10.1109/ispacs.2007.4445912Odorizzi, A. and Mazzini, G. (2008) M-GeRaf Analysis: Performance Improvement of a Multisink Ad-Hoc and Sensor Network Geographical Random Routing Protocol. Proceedings of the 16th IEEE International Conference on Software, Telecommunications and Computer Networks (SoftCOM’08), Split, 25-27 September 2008, 193-197. https://doi.org/10.1109/softcom.2008.4669478You, J., Lieckfeldt, D. and Timmermann, D. (2009) Tendency-Based Geographic Routing for Sensor Networks. Proceedings of the 6th IEEE Consumer Communications and Networking Conference (CCNC’09), Las Vegas, 10-13 January 2009, 1-2.
https://doi.org/10.1109/ccnc.2009.4784706Ghadimi, E., Khonsari, A., Talebi, M.S. and Yazdani, N. (2009) Loss-Aware Geographic Routing for Unreliable Wireless Sensor Networks. Proceedings of the 6th IEEE Consumer Communications and Networking Conference (CCNC’09), Las Vegas, 10-13 January 2009, 1-5.Zhu, X., Sarkar, R., Gao, J. and Mitchell, J.S.B. (2008) Light-Weight Contour Tracking in Wireless Sensor Networks. Proceedings of the IEEE 27th Conference on Computer Communications (INFOCOM’08), Phoenix, 13-18 April 2008, 1175-1183. https://doi.org/10.1109/infocom.2008.173Graham, R.L. (1972) An Efficient Algorithm for Determining the Convex Hull of a Finite Point Set. Information Processing Letters, 1, 132-133.
https://doi.org/10.1016/0020-0190(72)90045-2Preparata, F.P. and Hong, S.J. (1977) Convex Hulls of Finite Sets of Points in Two and Three Dimensions. Communications of the ACM, 20, 87-93.
https://doi.org/10.1145/359423.359430Andrew, A.M. (1979) Another Efficient Algorithm for Convex Hulls in Two Dimensions. Information Processing Letters, 9, 216-219.
https://doi.org/10.1016/0020-0190(79)90072-3Champ, J. and Saad, C. (2008) An Energy-Efficient Geographic Routing with Location Errors in Wireless Sensor Networks. Proceedings of the International Symposium on Parallel Architectures, Algorithms, and Networks (I-SPAN’08), Sydney, 7-9 May 2008, 105-110.Chen, B., Jamieson, K., Balakrishnan, H. and Morris, R. (2002) Span: An Energy-Efficient Coordination Algorithm for Topology Maintenance in Ad Hoc Wireless Networks. Wireless Networks, 8, 481-494.
https://doi.org/10.1023/A:1016542229220Sim, I. and Lee, J. (2008) Energy Effective Geographical Routing Considering Wireless Link Condition in WSN. Proceedings of the International Conference on Multimedia and Ubiquitous Engineering (MUE’08), Busan, 24-26 April 2008, 582-585.Noh, D., Yoon, I. and Shin, H. (2008) Low-Latency Geographic Routing for Asynchronous Energy-Harvesting WSNs. Journal of Networks, 3, 78-85.Li, J.L., Du, X., Xing, J. and Yang, Q. (2012) Location-Based Adaptive Routing Protocol for Underwater Acoustic Sensor Networks. Proceedings of the International Conference on Automatic Control and Artificial Intelligence (ACAI’12), Xiamen, 3-5 March, 1315-1319.Cha, J., Jeon, J., Kim, J. and Kwon, Y. (2010) Location-Based Multicast Routing Algorithms for Wireless Sensor Networks in Presence of Interferences. Proceedings of the IEEE International Conference on Communication Systems (ICCS), Singapore, 17-19 November 2010, 41-45.Popescu, A.M., Salman, N. and Kemp, A.H. (2014) Energy Efficient Geographic Routing Robust Against Location Errors. IEEE Sensors Journal, 14, 1944-1951.
https://doi.org/10.1109/JSEN.2014.2305832Popescu, A.M., Salman, N. and Kemp, A.H. (2013) Geographic Routing Resilient to Location Errors. IEEE Wireless Communications Letters, 2, 203-206.
https://doi.org/10.1109/WCL.2013.011713.120849Peng, B. and Kemp, A.H. (2011) Energy-Efficient Geographic Routing in the Presence of Localization Errors. Computer Networks, 55, 856-872.
https://doi.org/10.1016/j.comnet.2010.10.020Singh, P.K., Prajapati, A.K., Singh, A. and Singh, R.K. (2016) Modified Geographical Energy-Aware Routing Protocol in Wireless Sensor Networks. International Conference on Emerging Trends in Electrical Electronics & Sustainable Energy Systems (ICETEESES), Sultanpur, 11-12 March 2016, 208-212.
https://doi.org/10.1109/ICETEESES.2016.7581386