Principles of Mobile Communication
Principles of Mobile Communication
Simulation Modeling and Analysis
Simulation Modeling and Analysis
A coverage-preserving node scheduling scheme for large wireless sensor networks
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Optimizing Sensor Networks in the Energy-Latency-Density Design Space
IEEE Transactions on Mobile Computing
Dynamic Power Management in Wireless Sensor Networks
IEEE Design & Test
Voltage Comparator Circuits for Multiple-Valued CMOS Logic
ISMVL '02 Proceedings of the 32nd International Symposium on Multiple-Valued Logic
A study of energy consumption and reliability in a multi-hop sensor network
ACM SIGMOBILE Mobile Computing and Communications Review - Special issue on wireless pan & sensor networks
Network coverage using low duty-cycled sensors: random & coordinated sleep algorithms
Proceedings of the 3rd international symposium on Information processing in sensor networks
Medium access control with coordinated adaptive sleeping for wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Random Asynchronous Wakeup Protocol for Sensor Networks
BROADNETS '04 Proceedings of the First International Conference on Broadband Networks
ELECTION: Energy-efficient and Low-latEncy sCheduling Technique for wIreless sensOr Networks
LCN '04 Proceedings of the 29th Annual IEEE International Conference on Local Computer Networks
Design of Analog CMOS Integrated Circuits
Design of Analog CMOS Integrated Circuits
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
Energy efficiency of large-scale wireless networks: proactive versus reactive networking
IEEE Journal on Selected Areas in Communications
Sensor selection cost function to increase network lifetime with QoS support
Proceedings of the 11th international symposium on Modeling, analysis and simulation of wireless and mobile systems
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This work presents a novel algorithm to improve energy conservation in sensor networks. The algorithm is based upon Selective Activation of sensor nodes using Thresholding (SAT). The sensor continuously monitors the received signal and makes a binary decision to join the network if the average received signal power falls within the specified minimum and maximum threshold range. Thus, SAT divides all the receivers within the coverage range of the transmitter into sets of active and inactive nodes realizing a saving in power consumption proportional to the number of inactive nodes. The sensor life-time is enhanced by allowing the node to transmit at a power that is a constant fraction of the total residual energy. For two cases of linear and hexagonal networks considered in this work, it is shown that for each transmission, the fraction of inactive nodes may exceed by over 30% of the total number of nodes present within maximum one-hop distance of the transmitter. The cost associated with energy conservation through SAT is (a) Increased sensor node density or (b) Increased transmission power requirement.