Improving sensor network immunity under worm attacks: A software diversity approach

Sensor deployment is a fundamental issue in sensor networks. Studies on the deployment problem are concentrated deploying sensors to cover assigned areas or set of points efficiently. In this paper, we discuss the sensor deployment problem in the context of target tracking. Firstly, we propose a sensor deployment optimization strategy based on target involved virtual force algorithm (TIVFA). The TIVFA algorithm can dynamically adjust sensor networks configuration according to terrains, intelligence and those detected maneuvering targets in order to improve coverage and detection probability. Secondly, we present an improved sensor-ranking algorithm as well as a sensor protecting strategy with targets importance sequence in consideration. The TIVFA algorithm, together with the sensor protecting strategy, can produce an efficient and robust sensor network. Experiment results sufficiently demonstrate the effectiveness of the proposed approach.

In large-scale Wireless Sensor Networks (WSNs) the amount of data gathered require energy efficient data dissemination and data retrieval techniques. Data Centric Sensor (DCS) networks is a better approach in which the sensed data are sent to a sensor node whose name is associated with sensed data. Due to unattended nature of Wireless Sensor Networks, these sensor nodes are susceptible to different types of attacks. In this paper we propose a Secure Data Centric Sensor (SDCS) Networks that includes security and privacy support to DCS networks. In addition, we propose a multi-query optimization technique that aggregates similar queries and reduces the number of messages. Simulation and experimental results show that our work provides a secure data centric sensor network based on cryptographic keys and reduces the message overhead and incurs a minimum communication cost compared to previous works.

Ad-hoc and sensor network is a collection of two types of networks such as wireless sensor network (WSN) and wireless ad-hoc network (WANET). The combination of these two networks is known as ad-hoc and sensor network. The topology of the ad-hoc and sensor network is a combination of static and dynamic based on situation and problem. It is a collection of several sensor nodes along with some normal nodes. It consists a base station (BS) which acts as a router that provides the services to the customers by collecting sense information from the neighbor nodes. Although, there are several benefits of ad-hoc and sensor nodes, but it has also some limitations such as limited capacity of battery of the nodes, large number of deployable areas, resource constraints, variations of network parameters. The combination of these limitations causes several types of uncertainty and interference in the network that causes route breakage and data failure. In context of above-mentioned issues, optimization technique plays an important role in the context of single or multi-objectives. In multi-objective optimization, may be different objectives are conflicting one to another. In this paper, several swam intelligence techniques are compared with context of objective for giving better assessment. The name of these techniques are quasi-opposition dragonfly algorithm, quasi-opposition atom search optimization, salp swarm algorithm and quasi-opposition pathfinder algorithm. The results showed that the studied optimization techniques may be used for the WSN study and show good results.KeywordsWireless sensor networkWireless ad-hoc networkComputational intelligenceSwarm intelligenceSystem lifetimeCoverage

Hop-by-hop data aggregation is a very important technique for reducing the communication overhead and energy expenditure of sensor nodes during the process of data collection in a sensor network. However, because individual sensor readings are lost in the per-hop aggregation process, compromised nodes in the network may forge false values as the aggregation results of other nodes, tricking the base station into accepting spurious aggregation results. Here a fundamental challenge is how can the base station obtain a good approximation of the fusion result when a fraction of sensor nodes are compromised? To answer this challenge, we propose SDAP, a Secure Hop-by-hop Data Aggregation Protocol for sensor networks. SDAP is a general-purpose secure data aggregation protocol applicable to multiple aggregation functions. The design of SDAP is based on the principles of divide-and-conquer and commit-and-attest . First, SDAP uses a novel probabilistic grouping technique to dynamically partition the nodes in a tree topology into multiple logical groups (subtrees) of similar sizes. A commitment-based hop-by-hop aggregation is performed in each group to generate a group aggregate. The base station then identifies the suspicious groups based on the set of group aggregates. Finally, each group under suspect participates in an attestation process to prove the correctness of its group aggregate. The aggregate by the base station is calculated over all the group aggregates that are either normal or have passed the attestation procedure. Extensive analysis and simulations show that SDAP can achieve the level of efficiency close to an ordinary hop-by-hop aggregation protocol while providing high assurance on the trustworthiness of the aggregation result. Last, prototype implementation on top of TinyOS shows that our scheme is practical on current generation sensor nodes such as Mica2 motes.

Sensor devices have been widely applied to variety applications, including internet of things (IoT), cyber physical systems (CPS) and environmental monitoring systems. Because all sensing data in such applications are very important for data analysis and mining in a host machine, sensor devices are equipped with non-volatile memory (NVM) [1] and nonvolatile processor [2], [3] to prevent sensing data in sensor nodes from data loss. As a result, sensing data can be permanently stored in a sensor device without the risk of data loss even if the battery of the sensor device is run out. Based on such observations, the lifetime of a sensor network should be redefined due to non-volatile technologies, such as NVM and NV processor. Conventionally, the lifetime of a sensor network is the time at which the first sensor node uses up its battery due to the consideration of sensor network QoS [4], [5]. However, since sensing data can be permanently stored in a local drive, the lifetime of a sensor network should be redefined as the time at which the first out-of-date data is produced in a sensor node. If the battery of a sensor node is run out but the sensor node does not contain any out-of-date data, the sensor node will be regarded as a moribund node. If all moribund nodes can be rescued by a rechargeable technology, the sensor network will be immortal. Such observations motivate us to design an energy-efficient solution to achieve the goal of immortal sensor network by exploiting the benefits of wireless charging vehicle.

A large number of potential applications for Wireless Sensor and Actuator Networks (WSAN) have yet to be embraced by industry despite high interest amongst academic researchers. This is due to various factors such as unpredictable costs related to development, deployment and maintenance of WSAN, especially when integration with existing IT infrastructure and legacy systems is needed. Service-Oriented Architecture (SOA) is seen as a promising technique to bridge the gap between sensor nodes and enterprise applications such as factory monitoring, control, and tracking systems where sensor data is used. To date, research efforts have focused on middleware software systems located in gateway devices that implement standard service technology, such as Devices Profile for Web Services (DPWS), for interacting with the sensor network. This paper takes a different approach-deploying interoperable Simple Object Access Protocol (SOAP)-based web services directly on the nodes and not using gateways. This strategy provides for easy integration with legacy IT systems and supports heterogeneity at the lowest level. Twofold analysis of the related overhead, which is the main challenge of this solution, is performed; Quantification of resource consumption as well as techniques to mitigate it are presented, along with latency measurements showing the impact of different parts of the system on system performance. A proof-of-concept application using Mulle-a resource-constrained sensor platform-is also presented.

Mobility of sensor networks has been a subject of intense research over the last decade. Because of its low power, network based mobility has been proposed in order to maximize network life span. However in practice, majority of researches and implementations focus on sink node mobility while sensor nodes remain static. In this work, we reviewed the various methods of mobility scenarios and propose Network-Centric Optimal Hybrid Mobility Scheme for IPv6 Wireless Sensor Networks with Routing Protocol for Low-Power and Lossy Networks (RPL) as the underlying network protocol. It is a hybrid scheme which makes use of Cluster Sensor Proxy Mobile IPv6 (CSPMIPv6) and Network of Proxies (NoP) principles and interfaces IEEE802.15.4 and IEEE803.3. In this solution, we applied mobility to sensor nodes and allow the external network to control movement, handoffs and relocation while sensing. Our method improves latency by 42% over CSPMIPv6 and 27% over NoP. We also investigated the effect of the velocity of sensor nodes on the throughput, packet delivery and establish effective speed limit for operative Sensor Networks (SN) mobility and expands the network coverage without introducing additional nodes. Network life span is highly improved.

The paper describes the method for the teleoperation of the mobile robot through the wireless ad hoc sensor network in the environment such as disaster area. By using the sensor network, it is possible to communicate with the robot without using an existing communication infrastructure and use gathered information by the sensor network. The system configuration to use sensing information and routing on sensor network are discussed for the comfortable teleoperation. The teleoperation system by using sensor network device, mica2 mote, and omni-directional mobile robot are constructed and examined to confirm the utility of the system.

The host cardinality, defined as the number of distinct peers that a host communicates with, is an important metric for profiling hosts. Host cardinality distribution is very useful for characterising the communication connectivity patterns between hosts inside a network. With the development of the internet, network intrusion events occur frequently, such as worm propagation, DDoS attacks, port scanning, etc. These attacks generate a lot of traffic connections in a short time, resulting in network block and even paralysis. In the case of DDoS or worm attacks, the infected host usually produces a lot of connections with other hosts in a short period of time, then the host cardinality distribution will be different from normal situations. Hence, this paper proposes a hash-based algorithm for measuring the host cardinality distribution. Combining with hash, Bloom filter, and data stream algorithm, the space and time consumption of the algorithm is very small, so it can be used to estimate the host cardinality distribution in the high-speed network.

Distributed sensor data storage and retrieval have gained increasing popularity in recent years for supporting various applications. While distributed architecture enjoys a more robust and fault-tolerant wireless sensor network (WSN), such architecture also poses a number of security challenges especially when applied in mission-critical applications such as battlefield and e-healthcare. First, as sensor data are stored and maintained by individual sensors and unattended sensors are easily subject to strong attacks such as physical compromise, it is significantly harder to ensure data security. Second, in many mission-critical applications, fine-grained data access control is a must as illegal access to the sensitive data may cause disastrous results and/or be prohibited by the law. Last but not least, sensor nodes usually are resource-constrained, which limits the direct adoption of expensive cryptographic primitives. To address the above challenges, we propose, in this paper, a distributed data access control scheme that is able to enforce fine-grained access control over sensor data and is resilient against strong attacks such as sensor compromise and user colluding. The proposed scheme exploits a novel cryptographic primitive called attribute-based encryption (ABE), tailors, and adapts it for WSNs with respect to both performance and security requirements. The feasibility of the scheme is demonstrated by experiments on real sensor platforms. To our best knowledge, this paper is the first to realize distributed fine-grained data access control for WSNs.

The emergence of Wireless Sensor Networks has enabled the detection of potentially dangerous or catastrophic events prior to their actual occurrence. Wireless Sensor Networks rely on sensors that collect data on environmental conditions, allowing an early warning system to be implemented for potentially dangerous or catastrophic events, enabling an organization to take actions to prevent or mitigate these types of events. The technology digital twin has potential to improve ability of a wireless sensor network to detect faults and recover from them before the fault occurs. Digital Twins are real-time representations of physical objects or systems that track activity and behaviour of that physical object or system. By combining the Digital Twin with a Wireless Sensor Network, it is possible to create a model of predictive maintenance that can detect potential fault conditions before they occur by utilizing fault-analysis techniques. By utilizing this model to improve the Wireless Sensor Network, it is possible to improve the reliability and reduce the incidence of natural disasters. This report describes how Digital Twins can be created and how they can be used in Predictive Maintenance to increase the reliability of wireless sensor networks. These findings illustrate the power of such an AI-based approach toward completely transforming fault management within WSNs, toward being more robust, efficient, and reliable sensor networks in today's world.

A holistic sensor network design for energy conservation and efficient data dissemination

Sensor network simulators are important tools for the design, implementation and evaluation of wireless sensor networks. Due to the large computational requirements necessary for simulating wireless sensor networks with high fidelity, many wireless sensor network simulators, especially the cycle accurate ones, employ distributed simulation techniques to leverage the combined resources of multiple processors or computers. However, the large overheads in synchronizing sensor nodes during distributed simulations of sensor networks result in a significant increase in simulation time. In this paper, we present a novel technique that could significantly reduce such overheads by minimizing the number of sensor node synchronizations during simulations. We implement this technique in Avrora, a widely used parallel sensor network simulator, and achieve a speedup of up to 11 times in terms of average simulation speed in our test cases. For applications that have lower duty cycles, the speedups are even greater since the performance gains are proportional to the sleep times of the sensor nodes.

From experience with wireless sensor networks it has become apparent that dynamic reprogramming of the sensor nodes is a useful feature. The resource constraints in terms of energy, memory, and processing power make sensor network reprogramming a challenging task. Many different mechanisms for reprogramming sensor nodes have been developed ranging from full image replacement to virtual machines.We have implemented an in-situ run-time dynamic linker and loader that use the standard ELF object file format. We show that run-time dynamic linking is an effective method for reprogramming even resource constrained wireless sensor nodes. To evaluate our dynamic linking mechanism we have implemented an application-specific virtual machine and a Java virtual machine and compare the energy cost of the different linking and execution models. We measure the energy consumption and execution time overhead on real hardware to quantify the energy costs for dynamic linkin.Our results suggest that while in general the overhead of a virtual machine is high, a combination of native code and virtual machine code provide good energy efficiency. Dynamic run-time linking can be used to update the native code, even in heterogeneous networks.

Sensor networks are unattended deeply distributed systems whose database schema can be conceptualized using the relational model. Aggregation queries on the data sampled at each sensor node are the main means to extract the abstract characteristics of the surrounding environment. However, the non-uniform distribution of the sensor nodes in the environment leads to inaccurate results generated by the aggregation queries. In this paper, we introduce "spatial aggregations" that take into consideration the spatial location of each measurement generated by the sensor nodes. We propose the use of spatial interpolation methods derived from the fields of spatial statistics and computational geometry to answer spatial aggregations. In particular, we study Spatial Moving Average (SMA), Voronoi Diagram and Triangulated Irregular Network (TIN). Investigating these methods for answering spatial average queries, we show that the average value on the data samples weighted by the area of the Voronoi cell of the corresponding sensor node, provides the best precision. Consequently, we introduce an algorithms to compute and maintain the accurate Voronoi cell at each sensor node while the location of the others arrive on data stream. We also propose AVC-SW, a novel algorithm to approximate this Voronoi cell over a sliding window that supports dynamism in the sensor network. To demonstrate the performance of in-network implementation of our aggregation operators, we have developed prototypes of two different approaches to distributed spatial aggregate processing.