Energy efficient routing algorithm for wireless sensor networks

Kumar, Suneet; Kuila, Pratyay; Jana, Prasanta K.

doi:10.1201/9781315364094-39

Cited by 5 publications

(7 citation statements)

References 2 publications

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“…In the proposed protocol, a routing protocol [20] allocates the parent node, such that it satisfies the following constraints,…”

Section: B Data-appending Forwardingmentioning

confidence: 99%

“…For timeslot T0, in the same way as BACAT, TOSA starts with adding leaf nodes V5, V18, V1, V6, V7, V10, V3, V20, V21, and V9 to Q Ready , while setting Q Idle as an empty queue. TOSA then finds the lexicographical subsets such as (6,5,7,18), (5,18,20,10) etc. The algorithm computes the cost for each subset based on Eq.…”

Section: B Timeslot-optimaization Based Scheduling Algorithm (Tosa)mentioning

confidence: 99%

“…The algorithm computes the cost for each subset based on Eq. (27), and the lowest cost subset (5,18,20,10), the Langford subset, is allocated to T0. TOSA then adds the parents of allocated nodes to Q Idle and Q Ready .…”

Section: B Timeslot-optimaization Based Scheduling Algorithm (Tosa)mentioning

confidence: 99%

“…The nodes in the networks are randomly placed in the sensing area of 1000m × 1000m, with the sink node at the center. Each network is converted to an aggregation tree network by the energy-balanced aggregation routing algorithm [20]. The tree for each network is then provided to the scheduling algorithms.…”

Section: Simulation a Simulation Setupmentioning

confidence: 99%

“…Hence, the computational complexity of the placement of dominos in a box with minimum energy consumption can be expressed as Eq. (20) and solved through Eq. (21) and (22).…”

mentioning

confidence: 99%

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Energy Efficient Scheduling in Wireless Sensor Networks for Periodic Data Gathering

Kumar

Kim

2019

IEEE Access

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Nodes in the wireless sensor networks are battery operated, and thus, the efficient use of the node's energy during wireless communication is pivotal for the long battery life. This paper addresses the challenges in energy-efficient data aggregation and transmission scheduling for each node by using time division multiple access (TDMA). We introduce multi-channel TDMA scheduling algorithms with the objective of minimizing the total energy consumption in the network. The proposed algorithms utilize multiple radio channels to bestow efficient scheduling while eliminating collisions and overhearing. First, we formulate an integer linear programming (ILP) algorithm that finds the minimum bound for the network's energy consumption. Then, we propose a near-optimal heuristic algorithm based on backtracking, which uses memoization to spurn the suboptimal schedules. Subsequently, we propose a computationally efficient heuristic algorithm by using Langford subset generation. The algorithm reduces the energy consumption in each timeslot while avoiding revisiting the same timeslot. We conducted extensive simulations to evaluate three proposed algorithms. The simulation results demonstrate that the proposed heuristic algorithms provide performance comparable to the optimum results of the ILP algorithm and reduce the magnitude of computation time. INDEX TERMS Wireless sensor network, Internet of Things, meter reading, scheduling algorithm.

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“…In the proposed protocol, a routing protocol [20] allocates the parent node, such that it satisfies the following constraints,…”

Section: B Data-appending Forwardingmentioning

confidence: 99%

Section: B Timeslot-optimaization Based Scheduling Algorithm (Tosa)mentioning

confidence: 99%

Section: B Timeslot-optimaization Based Scheduling Algorithm (Tosa)mentioning

confidence: 99%

Section: Simulation a Simulation Setupmentioning

confidence: 99%

“…Hence, the computational complexity of the placement of dominos in a box with minimum energy consumption can be expressed as Eq. (20) and solved through Eq. (21) and (22).…”

mentioning

confidence: 99%

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