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Backpressure based scheduling has revealed remarkable performance in wireless multihop networks as reported in a lot of previous work. However, its lack of consideration on energy use efficiency is still an obstacle for backpressure based algorithms to be deployed in resource-constrained wireless sensor networks (WSNs). In this paper, we focus on studying the design of energy efficient backpressure based algorithm. For this purpose, we propose a gradient-assisted energy-efficient backpressure scheduling algorithm (GRAPE) for WSNs. GRAPE introduces a new link-weight calculation method, based on which gradient information and nodal residual energy are taken into account when making decisions on backpressure based transmission scheduling. According to the decisions made by this new method, packets are encouraged to be forwarded to nodes with more residual energy. We theoretically prove the throughput-optimality of GRAPE. Simulation results demonstrate that GRAPE can achieve significant performance improvements in terms of energy use efficiency, network throughput, and packet delivery ratio as compared with existing work. [ABSTRACT FROM AUTHOR]

Most applications of industrial wireless sensor networks (IWSNs) should converge process data generated by each node to the central manager. The data collection operation results in an important communication primitive referred to as convergecast. Convergecast is a many-to-one communication paradigm as a critical functionality deployed for industrial monitoring and control. Delaying of process data may degrade the overall control performance and even lead to the malfunction of industrial applications. Therefore, timeslot and channel resources should be scheduled efficiently for real-time communication. This paper is interested in determining a TDMA schedule that minimizes the number of timeslots and completes convergecast with a limited number of channels. In order to achieve the lower bound derived by theoretical analysis, we proposed a source aware scheduling algorithm for general network. For IWSNs with a fixed number of available channels, we present a source aware scheduling algorithm with constrained channel. According to simulation results, we demonstrate that the performance of our algorithm is close to the lower bound on latency with a limited number of channels. Our algorithm is also scalable for schedules with multiple packets and specific transmission latency of a single packet. [ABSTRACT FROM AUTHOR]

There is a growing interest in the use of video sensor networks in surveillance applications in order to detect intruders with low cost. The essential concern of such networks is whether or not a specified target can pass or intrude the monitored region without being detected. This concern forms a serious challenge to wireless video sensor networks of weak computation and battery power. In this paper, our aim is to prolong the whole network lifetime while fulfilling the surveillance application needs. We present a novel scheduling algorithm where only a subset of video nodes contributes significantly to detect intruders and prevent malicious attacker to predict the behavior of the network prior to intrusion. Our approach is chaos based, where every node based on its last detection, a hash value and some pseudorandom numbers easily compute a decision function to go to sleep or active mode. We validate the efficiency of our approach through theoretical analysis and demonstrate the benefits of our scheduling algorithm by simulations. Results show that in addition to being able to increase the whole network lifetime and to present comparable results against random attacks (low stealth time), our scheme is also able to withstand malicious attacks due to its fully unpredictable behavior. [ABSTRACT FROM AUTHOR]

Coverage is one of the key issues to achieve energy efficiency of a wireless sensor network. Sensor scheduling is one of the most important methods to solve coverage problems. It can ensure the coverage degree of a region and prolong the network lifetime. In this paper, we focus on the k-coverage scheduling problem to guarantee k-coverage sensing and network connectivity. We consider both deterministic and stochastic sensing models of the sensors and adapt the results of deterministic sensing model to solve the sensor scheduling problem under the stochastic sensing model. We use regular pentagons to divide the sensing disks to solve the k-coverage problem. Each sensor node runs a stochastic k-coverage-preserving scheduling algorithm to determine its work modes, and redundant nodes can enter into sleep mode, while active nodes ensure the coverage of the network. Theoretical analysis and simulation results show that our algorithm can reduce the number of active nodes and extend the network lifetime significantly while maintaining a given coverage degree. [ABSTRACT FROM AUTHOR]