IDSEP: a novel intrusion detection scheme based on energy prediction in cluster‐based wireless sensor networks

Han, Guangjie; Jiang, Jinfang; Shen, Wen; Shu, Lei; Rodrigues, Joel J. P. C.

doi:10.1049/iet-ifs.2012.0052

Cited by 44 publications

(24 citation statements)

References 25 publications

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“…In WRSN, most of the energy is consumed during the sending and receiving process. In this work, we adopt a simple energy model to obtain the energy consumption of a normal node . When sending or receiving a message with l bits, the energy consumption is shown in (1).

E = E_{normaltx} false(l, d false) + E_{normalrx} false(l false) = 2 \times E_{normalelec} \times l + ε_{normalamp} \times d_{ij}^{2}

where E elec indicates the energy consumption for sending or receiving each bit; d ij denotes the distance between the sender; and receiver; ɛ amp is the energy consumption for transmitting amplifier.…”

Section: System Modelmentioning

confidence: 99%

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Demand‐based charging strategy for wireless rechargeable sensor networks

et al. 2019

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A wireless power transfer technique can solve the power capacity problem in wireless rechargeable sensor networks (WRSNs). The charging strategy is a widespread research problem. In this paper, we propose a demand‐based charging strategy (DBCS) for WRSNs. We improved the charging programming in four ways: clustering method, selecting to‐be‐charged nodes, charging path, and charging schedule. First, we proposed a multipoint improved K‐means (MIKmeans) clustering algorithm to balance the energy consumption, which can group nodes based on location, residual energy, and historical contribution. Second, the dynamic selection algorithm for charging nodes (DSACN) was proposed to select on‐demand charging nodes. Third, we designed simulated annealing based on performance and efficiency (SABPE) to optimize the charging path for a mobile charging vehicle (MCV) and reduce the charging time. Last, we proposed the DBCS to enhance the efficiency of the MCV. Simulations reveal that the strategy can achieve better performance in terms of reducing the charging path, thus increasing communication effectiveness and residual energy utility.

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E = E_{normaltx} false(l, d false) + E_{normalrx} false(l false) = 2 \times E_{normalelec} \times l + ε_{normalamp} \times d_{ij}^{2}

Section: System Modelmentioning

confidence: 99%

“…In WRSN, most of the energy is consumed during the sending and receiving process. In this work, we adopt a simple energy model to obtain the energy consumption of a normal node [29]. When sending or receiving a message with l bits, the energy consumption is shown in (1).…”

Section: Energy Consumption Modelmentioning

confidence: 99%

Demand‐based charging strategy for wireless rechargeable sensor networks

et al. 2019

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“…Han et al propose IDSEP [11], which is an intrusion detection scheme based on energy prediction. Their energy consumption prediction model is based on Markov chains, similar to the Markov chain model in [18], but using five states instead of four.…”

Section: Related Workmentioning

confidence: 99%

Energy Map Model for Software-Defined Wireless Sensor Networks

Nunez¹,

Margi²

2017

Anais De XXXV Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Wireless sensor networks (WSN) is a technology commonly used for remote monitoring, tracking and detection applications. Since energy efficiency is a major concern in WSN, to know the remaining energy on each node could optimize the energy consumption. We propose a novel method to create an energy map for a Software-Defined Wireless Sensor Network (SDWSN). We implement an energy consumption prediction model into the controller, which obtains information of each node and estimates its energy consumption rate. This approach reduces the node processing overhead and memory usage when compared to other approach available.

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“…For example, Zhou et al [9] designed a new multimedia traffic classification and analysis method for handling the heterogeneity of diverse applications. Li et al [10] proposed an efficient intrusion detection protocol based on energy prediction in cluster-based WSNs. The aim of this protocol is to resist denial-of-service attacks due to the broadcast nature of wireless communication.…”

Section: Related Workmentioning

confidence: 99%

Anti-traffic analysis attack for location privacy in WSNs

Ying

Makrakis

Mouftah

2014

J Wireless Com Network

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Traditional encryption and authentication methods are not effective in preserving a sink's location privacy from a global adversary that is monitoring the network traffic. In this paper, we first propose a novel anti-traffic analysis (ATA) method to preserve the sink's location privacy. In order to confuse a local or global adversary, each node generates dummy messages, the number of which is dependent on the number of the node's children. Hence, ATA is able to prevent the adversary from acquiring valuable information on the sink's location through the traffic analysis attack. However, a larger number of dummy messages lead to consumption of extra energy. Then, we design our improved ATA (IATA) in such a way that we select some sensors to act as fake sinks, to ensure that sensors around fake sinks generate dummy messages and discard received dummy messages. Since the problem of the optimal fake sinks' placement is nondeterministic polynomial time (NP)-hard, we employ local search heuristics based on network traffic and security entropy. Performance analysis of the ATA scheme can protect the sink's location privacy, and IATA scheme can reduce energy consumption.

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IDSEP: a novel intrusion detection scheme based on energy prediction in cluster‐based wireless sensor networks

Cited by 44 publications

References 25 publications

Demand‐based charging strategy for wireless rechargeable sensor networks

Demand‐based charging strategy for wireless rechargeable sensor networks

Energy Map Model for Software-Defined Wireless Sensor Networks

Anti-traffic analysis attack for location privacy in WSNs

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