A Spawn Mobile Agent Itinerary Planning Approach for Energy-Efficient Data Gathering in Wireless Sensor Networks

Qadori, Huthiafa Q.; Zulkarnain, Zuriati Ahmad; Hanapi, Zurina Mohd; Subramaniam, Shamala

doi:10.3390/s17061280

Cited by 24 publications

(14 citation statements)

References 23 publications

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“…The authors in [35] proposed a mobile agent (MA)-based approach, which employs two algorithms: single-agent itinerary planning (SIP) and multi-ma itinerary planning (MIP). This approach is based on agent spawning.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Data Gathering Algorithm for Minimum Travel Route Planning in WSNs Based on Rendezvous Points

2019

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A recent trend in wireless sensor network (WSN) research is the deployment of a mobile element (ME) for transporting data from sensor nodes to the base station (BS). This helps to achieve significant energy savings as it minimizes the communications required among nodes. However, a major problem is the large data gathering latency. To address this issue, the ME (i.e., vehicle) should visit certain rendezvous points (i.e., nodes) to collect data before it returns to the BS to minimize the data gathering latency. In view of this, we propose a rendezvous-based approach where some certain nodes serve as rendezvous points (RPs). The RPs gather data using data compression techniques from nearby sources (i.e., affiliated nodes) and transfer them to a mobile element when the ME traverses their paths. This minimizes the number of nodes to be visited, thereby reducing data gathering latency. Furthermore, we propose a minimal constrained rendezvous point (MCRP) algorithm, which ensures the aggregated data are relayed to the RPs based on three constraints: (i) bounded relay hop, (ii) the number of affiliation nodes, and (iii) location of the RP. The algorithm is designed to consider the ME's tour length and the shortest path tree (SPT) jointly. The effectiveness of the algorithm is validated through extensive simulations against four existing algorithms. Results show that the MCRP algorithm outperforms the compared schemes in terms of the ME's tour length, data gathering latency, and the number of rendezvous nodes. MCRP exhibits a relatively close performance to other algorithms with respect to power algorithms.

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Section: Related Workmentioning

confidence: 99%

An Adaptive Data Gathering Algorithm for Minimum Travel Route Planning in WSNs Based on Rendezvous Points

2019

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“…To overcome the problem of direct approaches for large-scale WSNs, the idea of defining a set of sensor-nodes as Rendezvous Points (RPs) is proposed and still it is under development by considering different assumptions and employing new technologies in [ 9 , 11 , 32 , 33 , 34 ]. Generally, the mobile-sink visits the RPs instead of visiting the sensor-nodes one-by-one and other sensor-nodes will be assigned to suitable RPs that can minimize their communication range.…”

Section: Related Workmentioning

confidence: 99%

“…And at last, the trajectories in the subareas will be combined together for acquiring the whole network’s trajectory. In [ 34 ], Qadori et al studied the problem of implementing multiple mobile-sinks in the network based on the agent spawning by defining a main mobile-sink that is able to swam other mobile-sinks with various task assignments.…”

Section: Related Workmentioning

confidence: 99%

A New Path-Constrained Rendezvous Planning Approach for Large-Scale Event-Driven Wireless Sensor Networks

Vajdi

Zhang

Zhou

et al. 2018

Sensors

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We study the problem of employing a mobile-sink into a large-scale Event-Driven Wireless Sensor Networks (EWSNs) for the purpose of data harvesting from sensor-nodes. Generally, this employment improves the main weakness of WSNs that is about energy-consumption in battery-driven sensor-nodes. The main motivation of our work is to address challenges which are related to a network’s topology by adopting a mobile-sink that moves in a predefined trajectory in the environment. Since, in this fashion, it is not possible to gather data from sensor-nodes individually, we adopt the approach of defining some of the sensor-nodes as Rendezvous Points (RPs) in the network. We argue that RP-planning in this case is a tradeoff between minimizing the number of RPs while decreasing the number of hops for a sensor-node that needs data transformation to the related RP which leads to minimizing average energy consumption in the network. We address the problem by formulating the challenges and expectations as a Mixed Integer Linear Programming (MILP). Henceforth, by proving the NP-hardness of the problem, we propose three effective and distributed heuristics for RP-planning, identifying sojourn locations, and constructing routing trees. Finally, experimental results prove the effectiveness of our approach.

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“…The detailed migration path is designed by the administrator. This new computing mechanism can effectively reduce the network load, computational complexity, and improve the system stability and scalability [3]. The mobile agents perform data collection and analysis using the local computing resource, which is different from the traditional static agents and can greatly reduce the amount of data transferred from the terminal nodes to the control server.…”

Section: Introductionmentioning

confidence: 99%

IMLADS: Intelligent Maintenance and Lightweight Anomaly Detection System for Internet of Things

Qin

Wang

Chen

et al. 2019

Sensors

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System security monitoring has become more and more difficult with the ever-growing complexity and dynamicity of the Internet of Things (IoT). In this paper, we develop an Intelligent Maintenance and Lightweight Anomaly Detection System (IMLADS) for efficient security management of the IoT. Firstly, unlike the traditional system use static agents, we employ the mobile agent to perform data collection and analysis, which can automatically transfer to other nodes according to the pre-set monitoring task. The mobility is handled by the mobile agent running platform, which is irrelevant with the node or its operation system. Combined with this technology, we can greatly reduce the number of agents running in the system while increasing the system stability and scalability. Secondly, we design different methods for node level and system level security monitoring. For the node level security monitoring, we develop a lightweight data collection and analysis method which only occupy little local computing resources. For the system level security monitoring, we proposed a parameter calculation method based on sketch, whose computational complexity is constant and irrelevant with the system scale. Finally, we design agents to perform suitable response policies for system maintenance and abnormal behavior control based on the anomaly mining results. The experimental results based on the platform constructed show that the proposed method has lower computational complexity and higher detection accuracy. For the node level monitoring, the time complexity is reduced by 50% with high detection accuracy. For the system level monitoring, the time complexity is about 1 s for parameter calculation in a middle scale IoT network.

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A Spawn Mobile Agent Itinerary Planning Approach for Energy-Efficient Data Gathering in Wireless Sensor Networks

Cited by 24 publications

References 23 publications

An Adaptive Data Gathering Algorithm for Minimum Travel Route Planning in WSNs Based on Rendezvous Points

An Adaptive Data Gathering Algorithm for Minimum Travel Route Planning in WSNs Based on Rendezvous Points

A New Path-Constrained Rendezvous Planning Approach for Large-Scale Event-Driven Wireless Sensor Networks

IMLADS: Intelligent Maintenance and Lightweight Anomaly Detection System for Internet of Things

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