Recent technological advances allow us to envision a future where large numbers of low-power, inexpensive sensor devices are densely embedded in the physical environment, operating together in a wireless network. In event tracking applications of WSN, multiple sensors trigger simultaneously and need to inform sink continuously. This causes congestion over sink node. This paper provides multi-sink environment where the traffic is divided and the chance of congestion is avoided. We develop an NS2 simulator for IEEE 802.15.4 sensor network, and conduct several sets of experiments to study its features including: (1) PHY and MAC layer functionality such as association, disassociation, beaconing and data transmission, (2) Comparison between different types of application traffics, (3) Creating network with multiple sinks, (4) Performance analysis by means of packet loss rate.
WITH the popularity of laptops, cell phones, PDAs, GPS devices, RFID, and intelligent electronics in the post-PC era, computing devices have become cheaper, more mobile, more distributed, and more pervasive in daily life. It is now possible to construct, from commercial off-the-shelf (COTS) components, a wallet size embedded system with the equivalent capability of a 90's PC. Such embedded systems can be supported with scaled down Windows or Linux operating systems. From this perspective, the emergence of wireless sensor networks (WSNs) is essentially the latest trend of Moore's Law toward the miniaturization and ubiquity of computing devices.
A typical WSN [1] consists of spatially distributed autonomous sensors to cooperatively monitor physical or environment conditions. Selecting the optimum sensors and wireless communications link requires knowledge of the application and problem definition. Battery life, sensor update rates, and size are all major design considerations. Examples of low data rate sensors include temperature, humidity, and peak strain captured passively. Examples of high data rate sensors include strain, acceleration, and vibration. The envisioned applications of these wireless sensor networks range widely: ecological habitat monitoring, structure health monitoring, environmental contaminant detection, industrial process control, and military target tracking. Among them, the event tracking application attracts a lot of attention. Wild animal migration, Army march, Gas diffusion and Chemical pollution [2] are typical examples of area events. Those events will continuously trigger many sensor nodes to report data simultaneously to the sink node.
In traditional methods, centralized sink node is used to collect data from wide-spreading sensor nodes. As the number of sensor nodes concurrently reporting data increases, the unique sink node becomes bottleneck of the network. It causes congestion around sink node. In this paper, we assume that the sensor network is created with multiple sinks. So, the data traffic is dispersed among multiple sinks thus the chance of congestion is avoided.
The release of IEEE 802.15.4 represents a milestone in wireless personal area networks and wireless sensor networks. It targets low data rate, low power consumption and low cost wireless networking and offers device level wireless connectivity. We develop an NS2 simulator to analyse the features of IEEE 802.15.4 wireless sensor networks.
The rest of this paper is organized as follows: In section 2, we will review some related work regarding event contour tracking and congestion control schemes. In section 3, we elaborate the details of IEEE 802.15.4 WSN. In section 4, we give out the experimental results with discussion.
Related Work
Event Tracking
Event tracking is the process of deciding the moving objects in the network. Event tracking relies on persistently sensing and collecting the dynamic of the mobile event in a long period to acquire their attributes, such as event trajectory, etc. It needs collaborative communication and computation among multiple sensor nodes, since the information acquired by a single node is usually incomplete and inaccurate.
The requirements of object tracking are specified in the paper [3]. Such as detecting location, velocity and size of the object, sharing those information among multiple sensor nodes and inform to sink.
Any sensor that generates a signal dependent on distance from a target can be used for tracking. Accordingly different sensing modalities such as radar, acoustic, ultrasonic, magnetic, seismic, video, RF and infrared [4] have been considered for tracking applications.
In paper [5], binary sensors are used for target tracking. In this model, each sensor node performs detection and compares its measurement with a predefined threshold to determine whether an object is detected or not. From that, we can identify the location of the object.
In cluster based object detection [6], using voronoi diagram probabilistic value is calculated which depends on the length of the monitored voronoi edge. By using this probabilistic value object is detected.
Congestion Control
Area event tracking and multiple target tracking will trigger many sensors simultaneously, and therefore cause a lot of message traffic. That traffic is heavier around the sink node. Because sink node is unique and common end point of data flow for sensors, the region near the sink node has a high possibility to get congested.
The paper [7] provides buffer-based congestion avoidance. That is, any sensor node sends packet to another sensor node only when that node has buffer space to hold the data.
ESRT in paper [8] also provides congestion detection and avoidance scheme. This is also based on buffer overflow. In paper [9], the congestion detection algorithm sends notification to the source node if congestion is detected along the path. Then the source node readjusts the packet loading rate.
However these schemes have the weaknesses of heavy overheads and unnegligible data reporting delay. In this paper, we use multi-sink environment to avoid congestion.
IEEE 802.15.4 Wireless Sensor Networks
The new IEEE standard, 802.15.4, defines the physical layer (PHY) and medium access control sub layer (MAC) specifications for low data rate wireless connectivity among relatively simple devices that consume minimal power and typically operate in the Personal Operating Space (POS) of 10 meters or less. An 802.15.4 network can simply be a one-hop star, or, when lines of communication exceed 10 meters, a self-configuring, multi-hop network. Two different types of devices are defined in an 802.15.4 network, a full function device (FFD) and a reduced function device (RFD). An FFD can talk to RFDs and other FFDs, and operate in three modes serving either as a PAN coordinator, a coordinator or a device. An RFD can only talk to an FFD and is intended for extremely simple applications.
Physical Layer
The PHY layer provides an interface between the MAC sublayer and the physical radio channel. It provides two services, accessed through two service access points (SAPs). These are the PHY data service and the PHY management service. The PHY layer is responsible for the following tasks:
Activation and deactivation of the radio transceiver: Turn the radio transceiver into one of the three states, that is, transmitting, receiving, or off (sleeping) according to the request from MAC sublayer.
Energy detection (ED) within the current channel: It is an estimate of the received signal power within the bandwidth of an IEEE 802.15.4 channel.
Link quality indication (LQI) for received packets: Link quality indication measurement is performed for each received packet.
Clear channel assessment (CCA) for carrier sense multiple access with collision avoidance (CSMACA): The PHY layer is required to perform CCA using energy detection, carrier sense, or a combination of these two.
Channel frequency selection: Wireless links under 802.15.4 can operate in 27 different channels (but a specific network can choose to support part of the channels).
MAC Layer Functionalities
Generating network beacons if the device is a coordinator: A coordinator can determine whether to work in a beacon enabled mode, in which a superframe structure is used. The superframe is bounded by network beacons and divided into aNumSuperframeSlots (default value 16) equally sized slots. A coordinator sends out beacons periodically to synchronize the attached devices and for other purposes.
Synchronizing to the beacons: A device attached to a coordinator operating in a beacon enabled mode can track the beacons to synchronize with the coordinator.
Supporting personal area network (PAN) association and disassociation: To support selfconfiguration, 802.15.4 embeds association and disassociation functions in its MAC sublayer. This not only enables a star to be setup automatically, but also allows for the creation of a self-configuring, peer-to-peer network.
Employing the carrier sense multiple access with collision avoidance (CSMA-CA) mechanism for channel access: Like most other protocols designed for wireless networks, 802.15.4 uses CSMA-CA mechanism for channel access.
Handling and maintaining the guaranteed time slot (GTS) mechanism: When working in a beacon enabled mode, a coordinator can allocate portions of the active superframe to a device. These portions are called GTSs, and comprise the contention free period (CFP) of the superframe.
NS2 Simulation
NS2 is an object-oriented, discrete event driven network simulator developed at UC Berkeley. The ns simulator covers a very large number of applications, of protocols, of network elements and of traffic models. Ns simulator is based on two languages: an object oriented simulator written in C++, and a OTcl( an object oriented extension of Tcl) interpreter used to execute user’s command script.
Wireless Scenario Definition: It selects the routing protocol; defines the network topology; and schedules events such as initializations of PAN coordinator, coordinators and devices, and starting (stopping) applications. It defines radio-propagation model, antenna model, interface queue, traffic pattern, link error model, link and node failures, superframe structure in beacon enabled mode, radio transmission range, and animation configuration.
Three sets of experiments were conducted. In first experiment, different traffic (CBR, FTP) performance are analysed in a single sink environment. In second experiment, network is created with two sinks and packet loss rate due to sink failure is calculated for single and two sink environment and their results were compared. In third experiment, multi-sink environment is created.
Experiment 1:
A sensor network is created with specifications which are given in Table.1.
Table.1
Specifications
Nodes
4 including sink
Area
50mÃ-50m
Simulation time
50s
Routing
AODV
The output of this network is shown in Fig.1.
Fig-1: Single Sink Network
With this experiment three types of traffic have been created: (1) TCP over FTP with 5s to 10s duration, (2) UDP over CBR with 15s to 20s duration, and (3) Exponential over CBR with 25s to 30s duration. With the following diagram, we can conclude that FTP utilizes maximum bandwidth.
Fig-2: Traffic Comparison
Experiment 2:
The network is created with specifications in Table.1 but the numbers of sensor nodes taken as 8 with two sinks like below:
Fig-2: Network with Two Sinks
Packet loss rate is calculated for both Experiment 1 and 2. This occurred due to failed sink. So that the number of packets received is calculated for active sink and failed sink and results were compared which is shown in Fig.3.
Fig-3: Performance Analysis
Experiment 3:
High density network is considered in most sensor networks to guarantee the lifetime of the network also which ensures that coverage and connectivity. Here, 3000 sensor nodes are deployed and divided into four regions with assignment of separate sink node for each region. This avoids congestion over single sink. The multi-sink sensor network is given in Fig.4.
Fig-4: Multi-Sink Network
Future Work
The important challenge in WSN is energy efficiency. As in Experiment 3, we are considering high density network. If all nodes are active in such network, leads to wastage of energy. So, we have to find minimum sensors required to cover whole area thus provides energy efficiency. The second thing is, sink failure causes packet losses. Sink node failure detection and recovery is the future work of ours.
