Event-driven Wireless Sensor Networks Research Articles

A robust detection scheme against selective forwarding attacks in wireless sensor networks under variable harsh environments

Event-driven wireless sensor networks (EWSNs) often operate in harsh environments and are susceptible to selective forwarding attacks, where malicious nodes selectively drop packets. These attacks are difficult to detect due to similarities with normal node behavior under adverse conditions. To address this, we propose a detection scheme combining Variable Autoencoder (VAE) and Gaussian Mixture Model (GMM) with an Autoencoder (AE). The scheme extracts features from the node's single round forwarding rate (SFR), with VAE generating latent variables and GMM clustering them to differentiate behaviors. The AE model, trained on data representing normal behavior, uses reconstruction error to identify malicious nodes. Simulation results show that our scheme improves network throughput and achieves low False Detection Rate (FDR) and Missed Detection Rate (MDR), with both rates as low as 0.5% in normal conditions and below 2% in mobile harsh environments. With a computational complexity of O(n), this method is suitable for resource-constrained environments, demonstrating superior robustness compared to existing approaches.

A Combined Scheme Based on Artificial Immune System for Selective Forwarding Attack Detection in Event Driven Wireless Sensor Networks

A Combined Scheme Based on Artificial Immune System for Selective Forwarding Attack Detection in Event Driven Wireless Sensor Networks

Utilize DBN and DBSCAN to detect selective forwarding attacks in event-driven wireless sensors networks

Malicious nodes launching selective forwarding attacks jeopardize network reliability in event-driven wireless sensor networks. Swift detection and exclusion of these nodes are vital, especially in harsh environments where normal nodes also suffer from decreased forwarding rates due to poor channel conditions. To solve this problem, this paper proposes a deep belief network (DBN) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) based detection scheme against selective forwarding attacks under harsh environments. The DBN algorithm extracts and analyzes behavior features of nodes, while the DBSCAN clustering algorithm effectively distinguishes between malicious and normal nodes during selective forwarding attacks. Simulation results demonstrate the scheme’s effectiveness with a missed detection rate (MDR) of approximately 2% and a false detection rate (FDR) below 5% in harsh environments, providing a robust solution for ensuring the integrity and efficiency of data transmission in event-driven wireless sensor networks.

Cross-Layer Design for Energy-Efficient Reliable Multi-Path Transmission in Event-Driven Wireless Sensor Networks.

In event-driven wireless sensor networks (WSNs), a reliable, efficient, and scalable routing solution is required for the reliable delivery of sensory data to the base station (BS). However, existing routing algorithms rarely address the issue of energy efficiency under multi-path conflicts for multi-event-driven scenarios. In order to maximize energy efficiency while maintaining a manageable conflict probability, this paper investigates a cross-layer design of routing and power control for multi-event-driven WSNs. We first develop a mathematical characterization of the conflict probability in multi-path routing, and we then formulate the energy efficiency maximization problem as a non-convex combinatorial fractional optimization problem subject to a maximum conflict probability constraint. By utilizing non-linear fractional programming and dual decomposition, an iterative search algorithm was used to obtain near-optimal power allocation and routing solutions. Extensive results demonstrate that our proposed algorithm achieved a gain of 9.09% to 35.05% in energy efficiency compared to other routing algorithms, thus indicating that our proposed algorithm can avoid unnecessary control overhead from multi-path conflicts with a lower conflict probability and can ensure maximum energy efficiency through routing and power control design.

LSTM-NV: A combined scheme against selective forwarding attack in event-driven wireless sensor networks under harsh environments

Wireless Sensor Networks (WSNs) are susceptible to selective forwarding attacks, which can lead to reduced network efficiency and compromise the integrity of transmitted data. The indistinguishability between malicious behavior and normal packet loss in harsh environments exacerbates the challenge. To address this, in this paper, we provide a combined detection scheme called LSTM-NV, consisting of a training stage and a detecting stage. During the training stage, we integrate variational mode decomposition (VMD) with a long short term memory (LSTM) model to learn normal nodes’ forwarding behavior and then predict errors for each node. During the detecting stage, dynamic thresholds are determined to identify local anomaly points and a novel neighbor voting method is employed to differentiate between malicious and normal nodes. Our scheme demonstrates superior performance with a low average missed detection rate (MDR) of 0.6% and a low average false detection rate (FDR) of 3.3% compared to other effective methods, while also offering lower detection algorithmic complexity.

Source location privacy preservation in IoT-enabled event-driven WSNs

PurposePrivacy preservation is a significant concern in Internet of Things (IoT)-enabled event-driven wireless sensor networks (WSNs). Low energy utilization in the event-driven system is essential if events do not happen. When events occur, IoT-enabled sensor network is required to deal with enormous traffic from the concentration of demand data delivery. This paper aims to explore an effective framework for safeguarding privacy at source in event-driven WSNs.Design/methodology/approachThis paper discusses three algorithms in IoT-enabled event-driven WSNs: source location privacy for event detection (SLP_ED), chessboard alteration pattern (SLP_ED_CBA) and grid-based source location privacy (GB_SLP). Performance evaluation is done using simulation results and security analysis of the proposed scheme.FindingsThe sensors observe bound events or sensitive items within the network area in the field of interest. The open wireless channel lets an opponent search traffic designs, trace back and reach the start node or the event-detecting node. SLP_ED and SLP_ED_CBA provide better safety level results than dynamic shortest path scheme and energy-efficient source location privacy protection schemes. This paper discusses security analysis for the GB_SLP. Comparative analysis shows that the proposed scheme is more efficient on safety level than existing techniques.Originality/valueThe authors develop the privacy protection scheme in IoT-enabled event-driven WSNs. There are two categories of occurrences: nominal events and critical events. The choice of the route from source to sink relies on the two types of events: nominal or critical; the privacy level required for an event; and the energy consumption needed for the event. In addition, phantom node selection scheme is designed for source location privacy.

A reinforcement learning-based sleep scheduling algorithm for cooperative computing in event-driven wireless sensor networks

Emergency event monitoring is an important application of wireless sensor networks (WSNs). In the traditional cloud-assisted WSNs, the monitored data needs to be sent back to the cloud for processing. The round-trip of huge and burst data causes long delay and high energy consumption. With the increase of the computing capability of sensor nodes, local processing of events can be accomplished through cooperative computing between sensor nodes. However, if a node continues to undertake cooperative computing tasks, the energy consumption of nodes will be unbalanced and the network lifetime will be shortened. Sleep scheduling is an effective method to achieve energy balance, which can activate idle nodes alternately to participate in cooperative computing. In this paper, in order to make WSN more efficient to complete the local processing of events, we build a new system model based on node cooperative computing, and propose a multi-node Q learning-based cooperative computing node selection algorithm to obtain the sleep scheduling strategy. Simulation results show that compared with the classical algorithms, the proposed algorithm can complete more event processing, improve the reliability, and prolong the lifetime of WSNs.

Mobile sink-based data collection in event-driven wireless sensor networks using a modified ant colony optimization

The hotspot problem is one of the primary challenges in the wireless sensor networks (WSNs) because it isolates the sink node from the remaining part of the WSN. A mobile sink (MS)-based data acquisition strategy mitigates the hotspot problem, but the traditional MS-based data gathering approaches do not resolve the issue. However, the conventional techniques follow a fixed order of visits and static traversal of the MS. In this context, this paper uses a modified version of the ant colony optimization strategy for the data collected through a MS to mitigate the hotspot problem in the WSNs while improving the energy efficiency, network lifetime, throughput by reducing the packet loss and delay. In our work, we initially construct a forwarded load spanning tree to estimate the freight of each node in the WSN. Further, we choose RPs and their path simultaneously using the modified ACO algorithm by considering the forward loads, remaining energy, distance, etc. The proposed work also adopts the virtual RP selection strategy void unnecessary data exchanges between the nodes and RPs. Hence, it reduces the burden on relay nodes and optimize the energy usage among the nodes. We compare our approach with the recent ACO-based algorithms, and our approach outperforms them.

An Experimental Simulation of Addressing Auto-Configuration Issues for Wireless Sensor Networks

Applications of Wireless Sensor devices are widely used by various monitoring sections such as environmental monitoring, industrial sensing, habitat modeling, healthcare and enemy movement detection systems. Researchers were found that 16 bytes packet size (payload) requires Media Access Control (MAC) and globally unique network addresses overheads as more as the payload itself which is not reasonable in most situations. The approach of using a unique address isn't preferable for most Wireless Sensor Networks (WSNs) applications as well. Based on the mentioned drawbacks, the current work aims to fill the existing gap in the field area by providing two strategies. First, name/address solutions that assign unique addresses locally to clustered topology-based sensor devices, reutilized in a spatial manner, and reduce name/address size by a noticeable amount of 2.9 based on conducted simulation test. Second, name/address solutions that assign reutilizing of names/addresses to location-unaware spanning-tree topology in an event-driven WSNs case (that is providing minimal low latencies and delivering addressing packet in an efficient manner). Also, to decline the approach of needing both addresses (MAC and network) separately, it discloses how in a spatial manner to reutilize locally unique sensor device name approach and could be utilized in both contexts and providing an energy-efficient protocol for location unawareness clustered based WSNs. In comparison, an experimental simulation test performed and given the addresses solution with less overhead in the header and 62 percent fair payload efficiency that outperforms 34 percent less effective globally unique addresses. Furthermore, the proposed work provides addresses uniqueness for network-level without using network-wide Duplicate Address Detection (DAD) algorithm. Consequently, the current study provides a roadmap for addressing/naming scheme to help researchers in this field of study. In general, some assumptions were taken during the work phases of this study such as number of Cluster Head (CH) nodes is 6% of entire sensor nodes, location unawareness for entire sensor network and 4 bits per node address space which considered as the limitation of the study.

SEIRS model with spatial correlation for analyzing dynamic of virus spreading in event-driven wireless sensor networks

SEIRS model with spatial correlation for analyzing dynamic of virus spreading in event-driven wireless sensor networks

Performance Evaluation of LDCMAC for Wireless Sensor Networks

Wherever sensor nodes in each other’s contact range (CR) exist, the data packets may be transmitted in the same loop when a wireless sensor network (WSN) occurs. On a present contentious sequential MA protocol, so few nodes can forward their data packets through multiple layers in a single loop. Consequently, there has been a notable improvement in propagation time (E2ETD) with increasing nodes. The article suggests supporting event-driven WSNs with a low-delay containment-based contending sync MAC (LDCMAC) protocol. LDCMAC doubles the level of nodes that may send their databases within the same interval while nodes has data packets in one CR.LDCMAC is assessed and its performance is compared with CL-MAC protocol using NS-3 simulations. The findings show that the LDCMAC exceeds substantially the E2ETD and packet delivery ratio of CL-MAC.

Employ DBSCAN and Neighbor Voting to Screen Selective Forwarding Attack Under Variable Environment in Event-Driven Wireless Sensor Networks

In the event-driven wireless sensor networks (EWSNs), the event of interests occurs irregularly and at random in the network. Then, sensor nodes near the event sense the event and send out data packets of the event. Next, router nodes (RNs) forward those packets to the sink node (SN) by multi-hop communications. Compromised RNs would become malicious and launch selective forwarding attacks by dropping part of or all the packets from other nodes. On the other hand, a harsh environment makes the channel poor, so the routing nodes under a harsh environment have low packet forwarding rates because they sometimes have to give up forwarding the current packets after many tries to forward them due to poor channel. If the malicious nodes' forwarding rates become close to those of nodes under a harsh environment, the schemes based on packet forwarding rates for detecting selective forwarding attack may fail because they cannot differentiate the low data packet forwarding rates resulting from the malicious behaviors or harsh environment. To solve this problem, we provide a combined scheme for detecting selective forwarding attack in wireless sensor networks (WSNs) under harsh environments. This scheme employs a data clustering algorithm (DCA) to screen the malicious nodes out by clustering their cumulative forwarding rates (CFRs) and designs a voting decision method to protect the nodes under a harsh environment from being judged as malicious nodes. The simulation results show that our scheme has a low false detection rate (FDR) of 1% and a low missed detection rate (MDR) of 5% respectively with negligible energy consumption in WSNs under a local variable harsh environment.

An extended ACO-based mobile sink path determination in wireless sensor networks

In wireless sensor networks (WSNs), a mobile sink accumulate the data instead of routing directly to the sink to avoid the hotspot problem. In this process, it traverses a predetermined path by visiting a set of nodes called the rendezvous point (RP), and all the non-rendezvous points can transmit their data to the closest RP. Identifying the best collection of RPs and determining the mobile sink traveling path will decrease data loss and improve network performance. However, choosing a set of RPs and the route between them is a challenging task. It is more complicated in the event-driven applications due to the uneven data rate of SNs. In this context, we propose an extended ant colony optimization (ACO)-based mobile sink path construction for event-driven WSNs. In this, the best set of the RPs and the efficient mobile sink traveling path between them is determined. In addition to this, the RPs re-selection mechanism also adopted for balancing the energy between the nodes. After that, the virtual RPs are introduced to minimize the data transmissions between the sensor nodes and RPs. This process will improve WSNs’ performance in terms of reducing data losses while increasing network lifetime. The improved performance of the extended ACO-MSPD over existing is confirmed through simulation tests.

SIR Epidemic Model derived from Spatial Correlation for Worm Propagation in Event-driven Wireless Sensor Network

An event or a physical activity triggers many nodes for sensing the environment in wireless sensor networks for event monitoring applications. These triggered nodes then transmit the sensed data to the control center. To study the virus propagation behavior in event-driven WSNs, sensor coverage which is a quality of service parameter, can be considered in epidemic model design to get more insight. Existing epidemic models have global characteristics when it is considered a realistic behavior of WSNs. In this paper, a comparative analysis is carried out for spatial correlation feature found in sensor nodes based on sensing range. It is extended version of our recent work. We present analysis using Susceptible-Infectious-Recovered (SIR) epidemic model with and without spatial correlation feature. Firstly, we show that the correlated nodes are occurred due to sensing ability for event monitoring applications. These nodes are responsible for transmitting correlated information when an event occurs in the field. A detailed comparative analysis with recent existing SIR epidemic models is presented with results and discussions. A comparison based on basic reproduction number is also discussed. Our Results show the impact of spatial correlation in the behavior of virus spread with time. Comparative study shows the effective use of our model is in designing prevention mechanisms for infection control. It can also be used to study the virus spreading behavior for event-driven scenarios in WSNs .

MINIMIZING DELAY AND MAXIMIZING LIFETIME FOR WIRELESS SENSOR NETWORKS WITH ANYCAST

In this paper, we are interested in minimizing the delay and maximizing the lifetime of event-driven wireless sensor networks, for which events occur infrequently. In such systems, most of the energy is consumed when the radios are on, waiting for an arrival to occur. Sleep-wake scheduling is an effective mechanism to prolong the lifetime of these energy-constrained wireless sensor networks. However, sleep-wake scheduling could result in substantial delays because a transmitting node needs to wait for its next-hop relay node to wake up. An interesting line of work attempts to reduce these delays by developing any cast.-based packet forwarding schemes, where each node opportunistically forwards a packet to the neighboring node that wakes up among multiple candidate nodes. In this paper, we first study how to optimize the any cast forwarding schemes for minimizing the expected packet-delivery delays from the sensor nodes to the sink. Based on this result, we then provide a solution to the joint control problem of how to optimally control the system parameters of the sleep-wake scheduling protocol and the any cast packet-forwarding protocol to maximize the network lifetime, subject to a constraint on the expected end to end packet-delivery delay. Our numerical results indicate that the proposed solution can outperform prior heuristic solutions in the literature, especially under the practical scenarios where there are obstructions, e.g., a lake or a mountain, in the coverage area of wireless sensor networks.

An Energy-Aware and Load-balancing Routing scheme for Wireless Sensor Networks

<p>Energy and memory limitations are considerable constraints of sensor nodes in wireless sensor networks (WSNs). The limited energy supplied to network nodes causes WSNs to face crucial functional limitations. Therefore, the problem of limited energy resource on sensor nodes can only be addressed by using them efficiently. In this research work, an energy-balancing routing scheme for in-network data aggregation is presented. This scheme is referred to as Energy-aware and load-Balancing Routing scheme for Data Aggregation (hereinafter referred to as EBR-DA). The EBRDA aims to provide an energy efficient multiple-hop routing to the destination on the basis of the quality of the links between the source and destination. In view of this goal, a link cost function is introduced to assess the quality of the links by considering the new multi-criteria node weight metric, in which energy and load balancing are considered. The node weight is considered in constructing and updating the routing tree to achieve dynamic behavior for event-driven WSNs. The proposed EBR-DA was evaluated and validated by simulation, and the results were compared with those of InFRA and DRINA by using performance metrics for dense static networks.</p>

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

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.

Traffic Decorrelation Techniques for Countering a Global Eavesdropper in WSNs

We address the problem of preventing the inference of contextual information in event-driven wireless sensor networks (WSNs). The problem is considered under a global eavesdropper who analyzes low-level RF transmission attributes, such as the number of transmitted packets, inter-packet times, and traffic directionality, to infer event location, its occurrence time, and the sink location. We devise a general traffic analysis method for inferring contextual information by correlating transmission times with eavesdropping locations. Our analysis shows that most existing countermeasures either fail to provide adequate protection, or incur high communication and delay overheads. To mitigate the impact of eavesdropping, we propose resource-efficient traffic normalization schemes. In comparison to the state-of-the-art, our methods reduce the communication overhead by more than 50 percent, and the end-to-end delay by more than 30 percent. To do so, we partition the WSN to minimum connected dominating sets that operate in a round-robin fashion. This allows us to reduce the number of traffic sources active at a given time, while providing routing paths to any node in the WSN. We further reduce packet delay by loosely coordinating packet relaying, without revealing the traffic directionality.

Back-end scheduling methods of virtual clusters for energy conservation in event-driven WSNs

As sensor nodes of wireless sensor networks (WSNs) are generally battery-powered devices, the critical aspects to face concern how to conserve the energy for nodes, so that the network lifetime can be extended to reasonable times. In this paper, we aim at the energy conservation in event driven WSNs from the perspective of back-end scheduling for virtual clusters in cloud. Taking the Xen Credit scheduler as a baseline, we carry out a comparative study of three representative approaches (i.e., hybrid co-scheduling, balance scheduling, and dynamic switching-frequency scaling (DSS)). Then we discuss challenges in further optimising the energy conservation from the scheduling of virtual clusters. At last, we present and evaluate our proposed approach. Experimental results show that our approach outperforms the existing scheduling approaches of virtual clusters in the terms of reducing the energy consumption of WSNs.

A Real-time MAC Protocol for Event-driven Wireless Sensor Networks

A Real-time MAC Protocol for Event-driven Wireless Sensor Networks