The different characteristics of wirelesssensornetworks (energy limited, low-power computing, use of radio waves, etc...) expose them to many security threats . We can classify attacks in this type of network in two main categories: Active and Passive. In passive attacks, attackers are typically camouflaged, i.e. hidden, and tap the communication lines to collect data. In active attacks, malicious acts are carried out not only against data
Abstract: This work presents a data-centric strategy to meet deadlines in soft real-time applications in wirelesssensornetworks. This strategy considers three main aspects: (i) The design of real-time application to obtain the minimum deadlines; (ii) An analytic model to estimate the ideal sample size used by data-reduction algorithms; and (iii) Two data-centric stream-based sampling algorithms to perform data reduction whenever necessary. Simulation results show that our data-centric strategies meet deadlines without loosing data representativeness.
Technology advances in chip miniaturization, energy consumption and wireless communication have enabled the development and the deployment of new applications based on WirelessSensorNetworks (WSNs) . A WSN is an ad-hoc network composed of tiny devices with limited energy and computational resources, and it equipped with sensors in order to gather physical measures from the monitored environment. A lot of research effort has been spent on WSNs and many architectures [2,3] and protocols [4,5] have been developed. Typical civil WSNs are basically not complex monitoring systems, whose applications encompass environment and habitat monitoring [6,7], home automation [8,9], industrial sensing [10,11] and intelligent transportation systems [12,13]. In these WSNs, sensors gather the required information, mostly, according to a fixed temporal schedule, and send it to the sink, which interfaces with a server or a computer. Only at this point data from sensors can be processed, before being stored.
Energy expenditure is an important issue in wirelesssensornetworks due to the short span battery life. Reliable content delivery over a wireless channel is a major source of energy expenditure. The increasing wireless transmission rate results in a rapid increase of the energy consumption of wireless devices. This approach follows the Myopic scheduling algorithm and in this nodes selectively transmit data streams of different data sizes at different transmission rates so that the system reward can be maximized under given time and energy constraints (Gong et al., 2010). Scheduling strategy operates on an extremely fast time scale compared to the user dynamics, making it to natural to analyze the user level performance in continuous rather than discrete time and assume that the users are served simultaneously rather than in a time-slotted fashion (Borst, 2005). In dynamic scheduling (Manimaran and Murthy, 1998; Ramamritham et al., 1990), when new data packets arrive, the scheduler dynamically determines the feasibility of scheduling these new data
This work proposes and evaluates a sampling algorithm based on wavelet transforms with Coiflets basis to reduce the data sensed in wirelesssensornetworks applications. The Coiflets basis is more computationally efficient when data are smooth, which means that, data are well approximated by a polynomial function. As expected, this algorithm reduces the data traffic in wirelesssensor network and, consequently, decreases the energy consumption and the de- lay to delivery the sensed information. The main contribution of this algorithm is the capability to detect some event by adjusting the sampling dynamically. In order to evaluate the algorithm, we compare it with a static sampling strategy considering a real sens- ing data where an external event is simulated. The results reveal the efficiency of the proposed method by reducing the data with- out loosing its representativeness, including when some event oc- curs. This algorithm can be very useful to design energy-efficient and time-constrained sensornetworks when it is necessary to detect some event.
116 and hard locations. Localization (location estimation) capability is essential in most of the WSN applications, where, the sensed data is meaningless without the knowledge of precise location from where it is obtained. In addition, location awareness plays an important role in designing energy efficient routing protocols for wirelesssensornetworks [22, 23, 24]. Location of sensor nodes can be obtained either by placing the sensor nodes at points with known coordinates or by deployment of global positioning systems (GPS) on every sensor node. Since, the sensor nodes are randomly thrown in the sensing field in large numbers; they cannot be placed at the known location. Also, deployment of GPS on every sensor node is not feasible as it will escalate the cost of sensor network deployment. Therefore, wirelesssensor localization techniques are used to estimate the location of sensor nodes in the network using the apriori location knowledge of few specific sensor nodes deployed in sensing field, known as anchor nodes. The anchor nodes can obtain their location using global positioning system (GPS), or by placement at points with known coordinates. In application requiring knowledge of global coordinate systems, the anchors determine the location of sensor nodes with reference to the global coordinate system and the application where a local coordinate system is sufficient, the position of sensor nodes are referred to the local coordinate system of network. Many localization algorithms exist in the literature for location estimation of sensor nodes in WSN [1, 6, 12, 18, 23, 24]. The localization algorithms can be divided into two categories: (i) range- based localization techniques [7, 10, 18], and (ii) range-free localization techniques [4, 9, 12]. Range-based localization is defined by protocols that use absolute point
Transportation networks is another area where such improvement tools could be use- ful. Each day, we concern ourselves with being efficient with our resources and increas- ingly look for ways to improve productivity. One main concern is the time spent, each day, in transportation networks. It’s reduction is essential in order to become more effi- cient. As the population of larger cities increases, good urban transportation system must, therefore, be provided. Wirelesssensornetworks are expected to help on the planning of Urban Transportation Systems (UTS) [5, 6] and even aid blind in UTS . The core prob- lem is its complexity as there are numerous variables such as weather conditions, time, accidents, which makes it virtually impossible to predict the behaviour of traffic flow and hampers urban transportation scheduling planning.
Now a days intrusion detection, defense, climate control, medical systems ,environment monitoring, robotic exploration, smart spaces, disaster management, target tracking, wildlife habitat monitoring, scientific application, are uses the WirelessSensor Network. The Wirelesssensornetworks are made up of one or more battery-operated sensor devices with embedded processor, small memory and low power radio. Coverage and communication range for sensor nodes compared to other mobile devices is limited due to low power capacities of sensor nodes. Sensornetworks are composed of large number of nodes to cover the target area. Nodes in wirelesssensor network communicate with each other to give a common task .
A Stable Election Protocol) protocol was used to improve the LEACH protocol. It provides heterogeneity in the wirelesssensornetworks. This protocol provides LEACH like operation but this protocol has two different level of energy. Two tier clustering is used in SEP. in this approach, cluster head election is randomly selected and distributed based on the fraction of energy of each node assuring a uniform use of the nodes energy. SEP based on weighted election probabilities of each node to become cluster head according to their respective energy. In this some NCG are selected as cluster head and others as gateways. IB-LEACH distributes the energy load evenly among various sensor nodes. IB_LEACH is a self-organizing, adaptive clustering protocol. It does randomized rotation of the high-energy cluster-head position in such a way that various sensor nodes drain the energy from a single sensor. At any time sensor nodes can elect themselves to be a gateway. Base station confirms gateway nodes whether the node elected are suitable for gateway or not. Gateway nodes or cluster head nodes broadcast their status via advertisement message (ADV) to the other sensor nodes in the network. Non gateway nodes can also elect themselves as a cluster head with a certain probability. These cluster head nodes broadcast their status via advertisement message (ADV) to the other sensor nodes in the network.
Wireless Sensors are subjected to harsh deployment conditions and have constrained resources. In this paper, we analyse the effectiveness of LEACH protocol in extending the lifetime for energy- constrained wirelesssensornetworks. Based on LEACH protocol, an improved protocol termed as LEACH-R is proposed. LEACH-R improves the selection of cluster-head by considering the residual energy of the nodes during selection of cluster-head, thereby reducing the possibility of low-energy nodes being selected. Based on both residual energy and distance to base station, relaying node is chosen from clusterheads to become the relay node between base station and other cluster-heads. The simulation results suggest LEACH-R protocol could balance network energy consumption and extend the network life cycle more effectively as compared to LEACH.
Vertex covering has important applications for wirelesssensornetworks such as monitoring link failures, facility location, clustering, and data aggregation. In this study, we designed three algorithms for constructing vertex cover in wirelesssensornetworks. The first algorithm, which is an adaption of the Parnas & Ron’s algorithm, is a greedy approach that finds a vertex cover by using the degrees of the nodes. The second algorithm finds a vertex cover from graph matching where Hoepman’s weighted matching algorithm is used. The third algorithm firstly forms a breadth-first search tree and then constructs a vertex cover by selecting nodes with predefined levels from breadth-first tree. We show the operation of the designed algorithms, analyze them, and provide the simulation results in the TOSSIM environment. Finally we have implemented, compared and assessed all these approaches. The transmitted message count of the first algorithm is smallest among other algorithms where the third algorithm has turned out to be presenting the best results in vertex cover approximation ratio.
WirelessSensorNetworks (WSN) are constituted of a large number of tiny sensor nodes randomly distributed over a geographical region whose power consumption is low. How- ever, as shown in current research  , the classical rout- ing protocols are not applicable to sensornetworks in a real environment,mainly because of specific radio conditions. Noise, interference, collisions and the volatility of the node neighborhood leading to a significant drop in performance. Many applications for sensornetworks such as monitoring of forest fires, the remote meter reading,...For these cases,The Ge- ographic routing of data in this type of network is an important challenge, Geographic routing uses nodes locations as their addresses, and forwards packets (when possible) in a greedy manner towards the destination. Since location information is often available to all nodes in a sensor network (if not directly, then through a network localization algorithm) in order to pro- vide location-stamped data or satisfy location-based queries, geographic routing techniques are often a natural choice.
Wirelesssensornetworks (WSNs) have revolutionised today's practice of numerous scientific and engineering endeavours, including ecosystems, environmental sciences, military applications, scientific research etc. WSNs are used for sensing physical variables of interest at unprecedented high spatial densities and long-time durations . Applications like environmental monitoring, scientific research etc., explore the benefits of WSNs. Such applications require transferring a huge amount of sensed data from one point of the network to another. Considering the fact, that the energy consumed in transmission of 1 Kb of data over a distance of 100 meters is equal to the energy consumed in executing 300 million instructions with the rate of computation being 100 million instructions per second on a processor with general configurations , . Almost 70% of the total energy is consumed in communication within the network . Hence, the inherent constraints of WSNs such as limited bandwidth and limited battery life makes them prone to failure and compromise the network lifetime. Significant energy conservation in such networks can be achieved by: a) minimizing the cost of interaction between the nodes and b)
This chapter presents the main conclusions of this dissertation and points further research directions. The main objective of this dissertation was to design and development of an architecture to enable mobile devices, specifically smartphones, to remotely monitor wirelesssensornetworks in ubiquitous environments. This was accomplished with the construction of a Web service, a relational database, and an Android mobile application, used as a prototype to demonstrate, evaluate, and validate the proposed solution. Besides, end-to-end connectivity was established between the smartphone and the WSN through a gateway. To enable the user for being alerted when significant changes occur on the WSN, a push notification system was also developed and integrated in the mobile application. The constructed mobile application is able to present real-time and historical sensor readings in a simple and meaningful way as well as to receive push notifications. Therefore, all the dissertation objectives were successfully accomplished.
This thesis addresses the subject of performance assessment of real-time data management on wirelesssensornetworks. In fact nowadays, systems based on sensornetworks are getting increasingly used in many areas of the knowledge, giving rise to several flavours of applications such as financial market, human motion tracking application, monitoring of urban or environmental phenomena, patients monitoring in hospitals, automated production, military and aircraft control, etc. Some of these applications called real-time applications have the particularity of having to comply with the logical constraints and consistency imposed by the system, but also the temporal constraints related to the speed of execution of operations, as well as the respect of their deadlines. In addition, these applications must be able to handle large amounts of data, coming from sensors, necessary for their correct functioning. Thus, the use of databases is necessary and indispensable for this type of systems. However, unlike traditional databases, real-time databases must be able to also meet temporal constraints introduced by real-time systems, while ensuring the integrity constraints and consistency, the ability to share data, the recoveries after failures, etc., provided by the traditional databases management systems (DBMS). Thus, the real-time databases are essential for real-time systems with non-negotiable temporal constraints, such as automotive and aircraft applications where deadlines on temporal data and transactions can not be lost because of the risk of generating a disaster. Similarly, the real-time databases are useful for real-time systems running in unpredictable environments, such as financial market and human motion tracking application, where meeting most of the temporal constraints is the best system performance.
WirelessSensorNetworks (WSNs) consists of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions such as temperature, sound, vibration, pressure, motion or pollutants, at different locations. The development of wirelesssensornetworks was originally motivated by military applications for battlefield surveillance. Therefore, wirelesssensornetworks are used in many civilian applications, including environmental and habitat monitoring, health-care applications, home automation and traffic control. This network contains a large number of nodes which sense data from an impossibly inaccessible area and send their reports toward a processing center which is called “sink”. Since sensor nodes are power constrained devices, frequent and long-distance transmissions should be kept to minimum in order to prolong the network lifetime . Thus direct communication between nodes and the base station are not encouraged. Several communications Protocols have been proposed to realize power-efficient
Secure communication mechanisms in WirelessSensorNetworks (WSNs) have been widely deployed to ensure confidentiality, authenticity and integrity of the nodes and data. Recently many WSNs applications rely on trusted communication to ensure large user acceptance. Indeed, the trusted relationship thus far can only be achieved through Trust Management System (TMS) or by adding external security chip on the WSN platform. In this study an alternative mechanism is proposed to accomplish trusted communication between sensors based on the principles defined by Trusted Computing Group (TCG). The results of other related study have also been analyzed to validate and support our findings. Finally the proposed trusted mechanism is evaluated for the potential application on resource constraint devices by quantifying their power consumption on selected major processes. The result proved the proposed scheme can establish trust in WSN with less computation and communication and most importantly eliminating the need for neighboring evaluation for TMS or relying on external security chip.
A WirelessSensorNetworks (WSN) is a set of thousands of micro sensor nodes that are capable of sensing and establishing wireless communication. A wirelesssensor network consists of distributed sensors to monitor physical and environmental conditions which are of autonomous type. These types of sensors are used to measure temperature,pressure etc. The wireless sensors were initially used in military applications but nowadays it is used in many industrial and consumer applications for monitoring and controlling. It performs the computational and processing operations between two nodes. Wirelesssensornetworks generally provides us unique benefits in order to reduce the power consumed and in reducing the cost. The nodes in WSN are battery operated with sensing devices where energy resources are limited. When designing a power-efficient protocols the main issue that is wholly considered is to prolong the life time or to make the system energy efficient.
Abstract- The problem being tackled here relates to the problem of target tracking in wirelesssensornetworks. It is a specific problem in localization. Localization primarily refers to the detection of spatial coordinates of a node or an object. Target tracking deals with finding spatial coordinates of a moving object and being able to track its movements. In the tracking scheme illustrated, sensors are deployed in a triangular fashion in a hexagonal mesh such that the hexagon is divided into a number of equilateral triangles. The technique used for detection is the trilateration technique in which intersection of three circles is used to determine the object location. While the object is being tracked by three sensors, distance to it from a fourth sensor is also being calculated simultaneously. The difference is that closest three sensors detect at a frequency of one second while the fourth sensor detects the object location at twice the frequency. Using the distance information from the fourth sensor and a simple mathematical technique, location of object is predicted for every half second as well. The key thing to note is that the forth sensor node is not used for detection but only for estimation of the object at half second intervals and hence does not utilize much power. Using this technique, tracking capability of the system is increased.
Abstract— Recently, a concept of wirelesssensornetworks has attracted much attention due to its wide-range of potential applications. Wirelesssensornetworks also pose a number of challenging optimization problems. One of the fundamental problems in sensornetworks is the coverage problem, which reflects the quality of service that can be provided by a particular sensor network. The coverage concept is depending from several points of view due to a variety of sensors and a wide-range of their applications. One fundamental issue in sensornetworks is the coverage problem, which reflects how well a sensor network is monitored or tracked by sensors. In this paper, we formulate this problem as a decision problem, whose goal is to determine the degree of coverage of a sensor network, which is covered by at least k sensors, where k is a predefined value. The sensing ranges of sensors can be same or different. Performance evaluation of our protocol indicates that degree of coverage of wirelesssensornetworks can be determined within small period of time. Therefore energy consumption of the sensornetworks can be minimized.