Computing the kk-coverage of a wireless network

Coverage is one of the main quality of service of a wirelessnetwork. $k$-coverage, that is to be covered simultaneously by $k$network nodes, is synonym of reliability and numerous applicationssuch as multiple site MIMO features, or handovers. We introduce here anew algorithm for computing the $k$-coverage of a wirelessnetwork. Our method is based on the observation that $k$-coverage canbe interpreted as $k$ layers of $1$-coverage, or simply coverage. Weuse simplicial homology to compute the network's topology and areduction algorithm to indentify the layers of $1$-coverage. Weprovide figures and simulation results to illustrate our algorithm.Comment: Valuetools 2019, Mar 2019, Palma de Mallorca, Spain. 2019. arXiv admin note: text overlap with arXiv:1802.0844

Extending k-Coverage Lifetime of Wireless Sensor Networks Using Mobile Sensor Nodes

WiMob2009 : IEEE International Conference on Wireless and Mobile Computing, Networking and Communications , Oct 12-14, 2009 , Marrakech, MoroccoOne of the important issues in wireless sensor network (WSN) is to k-cover the target sensing field and to extend its lifetime. We propose a method to k-cover the field and maximize the WSN lifetime by moving mobile sensor nodes to appropriate positions for a WSN consisting of both static and mobile sensor nodes which periodically collect environmental information. Our target problem is NP-hard. So, we propose a genetic algorithm (GA) based scheme to find a near optimal solution in practical time. In order to speed up the calculation, we devised a method to check a sufficient condition of k-coverage of the field. For the problem that nodes near the sink node have to forward the data from farther nodes, we make a tree where the amount of communication traffic is balanced among all nodes, and add this tree to the initial candidate solutions of our GAbased algorithm. Through computer simulations, we confirmed that our method achieves much longer k-coverage lifetime than conventional methods for 100 to 300 node WSNs

Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered

Determination of RF source power in WPSN using modulated backscattering

A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants, at different locations. During RF transmission energy consumed by critically energy-constrained sensor nodes in a WSN is related to the life time system, but the life time of the system is inversely proportional to the energy consumed by sensor nodes. In that regard, modulated backscattering (MB) is a promising design choice, in which sensor nodes send their data just by switching their antenna impedance and reflecting the incident signal coming from an RF source. Hence wireless passive sensor networks (WPSN) designed to operate using MB do not have the lifetime constraints. In this we are going to investigate the system analytically. To obtain interference-free communication connectivity with the WPSN nodes number of RF sources is determined and analyzed in terms of output power and the transmission frequency of RF sources, network size, RF source and WPSN node characteristics. The results of this paper reveal that communication coverage and RF Source Power can be practically maintained in WPSN through careful selection of design parametersComment: 10 pages; International Journal on Soft Computing (IJSC) Vol.3, No.1 (2012). arXiv admin note: text overlap with arXiv:1001.5339 by other author

Optimal coverage multi-path scheduling scheme with multiple mobile sinks for WSNs

Wireless Sensor Networks (WSNs) are usually formed with many tiny sensors which are randomly deployed within sensing field for target monitoring. These sensors can transmit their monitored data to the sink in a multi-hop communication manner. However, the ‘hot spots’ problem will be caused since nodes near sink will consume more energy during forwarding. Recently, mobile sink based technology provides an alternative solution for the long-distance communication and sensor nodes only need to use single hop communication to the mobile sink during data transmission. Even though it is difficult to consider many network metrics such as sensor position, residual energy and coverage rate etc., it is still very important to schedule a reasonable moving trajectory for the mobile sink. In this paper, a novel trajectory scheduling method based on coverage rate for multiple mobile sinks (TSCR-M) is presented especially for large-scale WSNs. An improved particle swarm optimization (PSO) combined with mutation operator is introduced to search the parking positions with optimal coverage rate. Then the genetic algorithm (GA) is adopted to schedule the moving trajectory for multiple mobile sinks. Extensive simulations are performed to validate the performance of our proposed method