A NOVEL DISTRIBUTED APPROACH TO DETECT CUT VERTEX IN WIRELESS SENSOR NETWORKS
SHIKHA CHOURASIYA Department of Computer Science
GGITS Jabalpur, India ranjeetchourasia129@gmail.com
SHITANSHU JAIN
Department of Computer Science GGITS Jabalpur, India shitanshujain@ggits.org
Abstract— Wireless sensor networks (WSNs) have emerged as a promising new technology to monitor large regions at high spatial and temporal resolution. A node may fail due to a variety of conditions such as mechanical or electrical problems, environmental degradation, and battery reduction. Failure of a set of nodes will reduce the number of multi-hop paths in the network. Such failures can cause a subset of nodes – that have not failed – to become disconnected from the rest of the network, resulting in a partition of the network also called a
“cut”. Two nodes are said to be disconnected if there is no path between them. Sensors become fail for several reasons and the network may breaks into two or more divided partitions so can say that when a number of sensor fails so the topology changes. In fact, node failure is expected to be quite common anomaly due to the typically limited energy storage of the nodes that are powered by small batteries. Failure of a set of nodes will reduce the number of multi-hop paths in the network. We propose a distributed algorithm, an asynchronous distributed algorithm to detect lost connectivity means cut in a Wireless Sensor Networks. And this algorithm has been analyzed through simulation by calculating communication cost and time delay.
Keywords—Cut Vertex, network partitioning, wireless sensor networks.
I. INTRODUCTION
Wireless sensor network is basically a combination of a base station that is powerful enough and a low-end sensor node set. Both of the station and sensor nodes have features of wireless network and send information based on a multi-
hop, ad-hoc wireless network. Wireless sensor networks (WSN) is developed as an important technology for various applications like incrementing and observation of the real world. A WSN is a network of nodes that are usable for a
fixed range of area. These sensor nodes are capable enough to communicate with each other and also that is capable to communicate directly with the base station. The sensor systems basically measure the environmental systems and change it into an electrical signal. This system provides us a way to find condition of the system or a real object. The base of these networks is a small microprocessor build up with one or many MEMS (micro- electromechanical system) sensors, a wireless transceiver and an actuator. These systems are able to be embedded or scattered in physical world, in large quantities, and by which they can manage itself into a wireless, ad hoc and multi-hop network, that give us permission to observe the physical world at an unprecedented spatial and temporal resolution [4]. A wealthy multiplicity of scientific, commercial, and military has been developed for sensor network systems, and many experiment based models are still developing in academic and industry. Wireless sensor networks (WSNs) have developed as an important new technology to monitor large regions at high spatial and temporal resolution.
Virtually any physical variable of interest is able to be monitored by embedding a wireless system with a sensor and grouping of these sensors together with the
help of their features of on-board wireless communication. WSNs are able to having a large effect on diverse applications in the area of civil and defense. Network of these sensors is able to assist rescue operations by finding out the location of survivors, also it is able to identify risky regions, and alert the rescue team members to be more aware of various situations of disaster area.
WSN system is an important part of wireless ad hoc network that is for providing wireless communication. In WSN, sensor systems are distributed in a specific area. The development of nano- technology made it an important and advantageous technologically that is feasible and financially able to develop small and limited-power systems that combine basic observation to features of multi-purpose sensor and wireless communication systems. It is preferred that these tiny systems are sensor devices, which is portable and advantageous. These nodes are spread in a bulk in the real world, where they are well managed and self-organize into an ad hoc network.
Ad hoc network is a multi-hop connectionless network. Sensor devices are embedded with connectionless interfaces by which they can establish proper communication with each other to create a group or a network. Sensor devices communicate with each other
through a wireless connection and they make a proper and self-organized network after deploying in an ad hoc network manner. Thousands and even ten thousands Systems or nodes are estimated.
Such nodes can be developed in the way we live and work. In today’s world, wireless sensor networks are using at an emerging pace. We can say that, may be after some time the world would be based on wireless sensor networks. This can be said as the Internet is being a physical network. These systems are giving internet services with large potential for several observing application areas which include environment based, medical based, military system, transportation facility, entertainment, crisis management, and smart places. Sensor network is also very good for prediction of system operations and network failure management. WSN are a new phase of distributed systems that are an important part of the real world space to which they belong. Sensor networks are a large collection of nodes.
Every node is autonomous and has short range; collectively they are co-operative and effective over a large area.[7] Now Sensor devices are used to sense the world’s physical states such as, sound or light intensity , temperature or proximity to real world objects.
I. ARCHITECTUREOFWSN When designing any wireless sensor network, we need to concern on many different factors like less power consumption, scalability of network and fault tolerance capability.
The two basic kinds of sensor network architecture are [6]:
(i) layered architecture (ii) clustered architecture
In the first one; Layered architecture of WSN‟s, we have a single and powerful base station and around it, the layers of sensor systems that for nodes that contains the similar hop count as the BS. The base station employed as an access point and gives wireless communication by using small nodes and devices.
On the other hand, sensor devices are managed in clusters in the clustered architecture and managed and maintained by cluster head, which means each n every cluster head may have many sensor devices. If the sensing devices of cluster need to send the message then it will first send the message to their basic cluster heads and then these heads send the message to a base station which is normally an access point that is directly fixed to a wired physical network. Cluster based architecture is very important in the matter of data fusion for sensor network.
Sensor network is basically a self-
organized path, so the formation of cluster as well as selection of cluster heads need to be a distributed and autonomous process. Every device of the wireless sensor network has:
1. processing capability (one or more microcontrollers, CPUs or DSP chips),
2. Many types of memory
3. May also have a RF transceiver 4. May have a power source, and accommodate various sensors and actuators.
Systems of 1000s or even 10,000 nodes are anticipated. Such systems can revolutionize the way we live and work.
[9]
II. MOTIVATION
In WSN, energy efficiency becomes one of the core problems due to low power support for the resource-limited sensor nodes. Recently, improvements in hardware technology have resulted in low- cost sensor nodes, which are composed of a single chip embedded with a memory, a processor, and a transceiver. Low power capacity in sensor nodes leads to limited coverage and communication range in
comparison to other mobile devices. Due to various limitations arising from the limited size of sensor nodes and ad hoc method of deployment, each sensor has energy-constrained batteries. In addition, it is generally infeasible or inconvenient to charge/replace exhausted batteries, since nodes may be deployed in a hostile or impractical environment. Short sensing ranges result in dense networks and thus it is necessary to design an energy efficient routing protocol in WSN. Since the communication module is the most energy consuming part in WSNs, it is a primary concern to minimize communication while achieving the desired network operation.
III. PROBLEM STATEMENT
One of the unique challenges in mobile ad- hoc networking environments is the phenomenon of network partitioning, which is the breakdown of a connected network topology into two or more separate, disconnected topologies [1]
Similarly sensors become fail for several reasons and the network may breaks into two or more divided partitions so can say that when a number of sensor fails so the topology changes. A node may fail due to a variety of conditions such as mechanical or electrical problems, environmental degradation, and battery reduction. In fact, node failure is expected to be quite
common anomaly due to the typically limited energy storage of the nodes that are powered by small batteries. Failure of a set of nodes will reduce the number of multi- hop paths in the network. Such failures can cause a subset of nodes – that have not failed – to become disconnected from the rest of the network, resulting in a partition of the network also called a “cut”. Two nodes are said to be disconnected if there is no path between them. [17].
IV. RELATEDWORK
Researchers have raised the problem of losing connectivity in sensor networks in several papers, but there is still a need of having formal investigation over such an issue. Since, a very long time finding the cut device in a graph has been studied, in graph theory. Because of the crashed nodes or CUT nodes WSNs can be divided into several connected nodes. We have cut detection systems and techniques but are positioned only for connection oriented networks. In the research work proposed by Yunnan Key, which is” Efficient Constraint Monitoring Using Adaptive Thresholds Detecting constraint violations in large-scale distributed systems” has attracted attention of many researchers in recent times because of its variety of applications (security, network monitoring, etc.). The performance and efficiency of
connectivity of these systems is a serious concern and regulates their practicality. In this research work, Srinivas Kashyap presented a new set of approaches called non-zero slack systems to implement dispersed SUM queries proficiently. He described logically as well as empirically, that these approaches can results to a substantial decrease in the communication among devices. We suggest 3 adaptive non-zero slack systems that adapt to altering information distributions. The best approach among them is a lightweight reactive approach that probabilistically maintains local constraints that are centered on the existence of some events.
We conduct a general investigational research using real-life and synthetic information sets, and show that our non- zero slack systems suffer significantly low communication overhead compared to the state of the art zero slack scheme .[1] To detect cuts a distributed algorithm proposed by[11] , known as the Distributed Cut Detection (DCD) algorithm. The procedure permits all devices to sense DOS actions and a subset of system to detect CCOS proceedings.
The procedure we suggest is distributed and asynchronous: it includes only communication between directly connected devices which are local, and it is durable to temporary connectivity crash
between two devices. As a result, here the cuts detection takes place. A key element of the DCD procedure is a distributed iterative computational algorithm through which the devices calculate their electrical potentials. P.Barooh told if a device U is detached from the source system, then it is known that disconnection from source device leads DOS event that has appeared for U.
A cut occurs somewhere in the network we assume it as a connected node, because that cut node does not separate a device U with the source sensor device, but a Cut in the network occurred somewhere (CCOS) when any action has occurred for the node U. The meaning of cut detection is:
(i) Sensing all nodes of a DOS event whenever any action happens, and
(ii) Detection of the estimated location of the cut node and CCOS events by the devices that are near to a cut.
The “estimated location” of a cut means the location of one or multiple active devices that are placed at the boundary of the cut node also that are linked to the source. Systems that detect the happening and approximate locations of the cut nodes can then send alert messages to the source node or the base station. [8] In the research paper [10] Chong and Kumar studied this problem with a security concern. The
sensor networks may function in hostile environments and structures to sense tampering that needs to be built into the design. This work concludes that some recent research results in sensor network system; it also includes localized algorithms and directed dispersion, distributed tracking in wireless ad hoc networks, and distributed classification using local agents [10]
An algorithm based on BFS for cut links detection is presented but the cut link detection is not same as to cut node detection. Also we should know this fact that when a node is detected as a cut vertex, then not any link that is incident on it is a cut edge and also if a link that is incident on a node is detected as a cut edge, then that node is not a cut device.
DDFS is a tree based method, in this, whenever a message goes to a node, a counter is incremented. All child nodes send this message to the parent device and parent device gathers the indices transmitted by its children. If these indices, which are received by parent device, are smaller than the indices of parent node, then that parent node is known as a cut vertex. Time interval is much more as DDFS has to cross the links sequentially.
DFS in is a typical procedure. To detect all the cut nodes in a given graph, DFS executes a DFS traversal on the graph,
throughout which it searches out all the back side links which are connected to a device and its ancestor.
V. CUTSOFACONNECTION ORIENTEDNETWORK
In the research paper, he developed the techniques and algorithm to detect the cuts and particular places of partitioning in the network [1]. In his work explained an easy and less hectic method to detect and locate the cut nodes or links or breaks of the sensor networks. In their research paper, they worked to decrease the cost of connectivity for sensing and finding out sequential cuts and for this they use only a small number of sentinel devices. The group of these kinds of nodes helps to reduce the communication costs, and also impacts as a fine second-order optimization. One more such type of technique for reducing the cost of connectivity in the network is to create the sensing of cuts dispersed. These explained challenges are very practical questions and also good directions for future work. In this work, we have done studies only about linear or sequential cuts. This category of cuts or partitioning is considered as a very important and natural class of cuts. A fine and more rich class of cuts and breaks would involve rectangular, circular or polygon cuts [1].
VI. PROPOSED METHOD
We propose a distributed algorithm, an asynchronous distributed algorithm to detect lost connectivity means cut in a Wireless Sensor Network .This algorithm employs a coded spanning tree to reduce communication cost, and assume a condition for detecting the critical node.
This can be tested on all nodes in parallel so as to reduce time delay.
(1) A distributed algorithm is proposed to detect the cut node in WSNs.
(2) Algorithm traverses the nodes in a network in parallel and use of the edge color concept on the coded spanning tree for cut vertex detection.
To solve disconnectivity in wireless sensor network we propose an asynchronous distributed algorithm.
Algorithm:
A sensor network is modeled as a graph G
= (V, E) whose node set V correspond to the wireless sensors and whose edges E consists of pairs of nodes (u, v) that can communicate directly with each other. A wireless sensor network can be like a connected but undirected graph. Those edges are the links which connects sensor nodes. A Cut can be defined as to find a node that connects Two other different nodes. Like if we remove a node u than x and y is disconnected. So u is called a cut
vertex. Where x, y and are the part of graph V. x, y €V (G).
So a cut can be defined as set of cut vertices in a graph G.As our algorithm propose to detect the cuts in a wireless sensor network. Like in above diagram V1 is a cut because if we remove V1 from the graph then network separates into two different parts.
1). Spanning Tree and Edge Coloring Method
In this process firstly we are developing a spanning tree from a graph G=(V,E).In this process firstly a node sends a probe message to all the neighbor nodes including its child node .Like, in graph a node Sink S sends a probe message to all its set of neighbors node N(u),its child node C(u).If node receives this probe message first time then that node sets the sender as a parent node and multicast the message to the entire neighbor node. Like if a node U receives a probe message from S than U sets sender S as a parent node and node U multicasts the message to all its neighbors. And sends a confirm message to parent node and node u acknowledges a confirm message to its parent node that announce that sender as its parent.
And if any node receives the probe message more than one time than node adds sender as a friend node means node U
adds sender as a friend node and sends a friend message to the sender.
After developing a spanning tree from a graph G=(V,E) through the previous process now we move to the next process for finding the cut which is called a coding mechanism, which provide a code to all the nodes. Each node would have the code [s, e] for each node U in the spanning tree.
For the coding to each node we should know how many nodes belong to a root, like a leaf node do not have any node, so it sends a message to its parent node, and count value=1.And after receiving this message from all the child means from the leaf node, parent node compute all the count value and then sends this value to next parent node i.e.
│Tsub(U)│=∑v€c(u)│Tsub(V)│+1
As we see in the above step a parent node calculates the total number of nodes with the help of its child node’s message.
Means with the help of above formula node U calculate the total number of node .And forward to the next level. And if parent node is sink S, means sink S receives the message containing count value from all its child means it would have │Tsub(V1)│ │Tsub(V2)│…..
│Tsub(Vn)│ from all its child v1,v2…vn.
And after receiving these value it
calculates that how many nodes belong to it with the help of SUMi = ∑j=1 to i
│Tsub(Vj)│ Where i is 1≤i≤m. And initialize SUM0=0
Where the value of i belong to starting child to maximum child. As we see in the example Sink S assigns itself a code [0,SUMm] and multicast o all the nodes and also sends an offset number to all the child, i.e. Ovi=1+SUMi-1
Then node V1 receives it and calculate Ovi=1+SUM0
->Ov1=1+0=1;
So offset number for Vi is 1, and now calculate the total number nuber of node
belong to Vi.
SUM(Vi)= ∑j=1 to i │Tsub(Vj)│
So the value for SUM(V1)=4;
Now assigns the code [Ou, Ou+SUMm]
and after evaluating SUMm means SUM(Vi) the code is [1,5] for node V1.And this assign message is sent to all the friends and multicast to all child node and when the second node receives an offset number Ovi=Ou+1+SUMi-1 where previous offset is also included.
2). Cut Finding Algorithm
To color the edge and then compare the color of several edges. If the number of color is more than one of a particular node then it will be a Cut node and if the colors
of all edges are same means number of colors for all edges for a particular node is only one then node does not belong to a Cut node.
But question arise that on the basis of color how would we find out Cut? And how would we compare the colors of edges?
The answer is that we will use a coding system in our algorithm which provides a code to each node in the spanning tree.
And after coding to the each node in the tree we compare the code. And this coding system we are using for describing the position of the node in the tree. Here we would have two fields[S,E] which tell the position of the system.
Using the edge coloring method in the spanning tree, color each edge with a particular value suppose we have a path x- y and color it with a color z then can say color XY=Z. So with the help of this process we set each node colors its parent edge with the same value like node NP=0 and where P is N‟s parent node. And NC=1 for each child edge.
After coloring to the each edge now each node will share their code with their friend node through a message UP (U).So a message will contain code of the node.
Suppose we have a node U ant it’s code is [Su,Eu].And U have a friend node V and V’s code is [Sv,Ev].So both friends
exchange their code through a UP(U) message. After exchanging the coded message with friend node ,if any node have the child then node will be waiting for receiving the coded message and if node does not have any child node means it’s a leaf node then this leaf node will send the message UP(U) to the parent node. And when the parent node receives the UP(U) message from child node compare the message’s code with the parent node code. For the comparison there are two conditions:-
1) eu − su = |Tsub(u)| − 1, and
(2) For node v with code [sv, ev], u is an ancestor of v in T(s) iff [sv, ev] ⊆ [su, eu], i.e. su ≤ sv and eu ≥ ev
So now on the basis of above calculations now compare coded message with the parents’ code.
Suppose a parent node U with code [Su,Eu] receives an coded message [s,e]
from a child node.
If [s,e] ⊆ [Su,Eu] then node U search those node which have [s,e] ⊆[Sx,Ex] then sets the color
UX=UP and UP=UY. And if [s,e]
!⊆[Su,Eu] then U adds [s,e] in the coded message and sets
UP=UY.
And the more important is that if any node does not have any friend node means it
would not have any coded message so it have empty value for the coded message and node with empty message sends to the parent node and sets the different color so at this point node could be a cut node.
VII. RESULTS
Figure Shows, there are 10 nodes and from the node 0, 1,2,3,4 we are finding the cut vertices or node. So at the start of animator we can see on the basis of coding mechanism use the coloring method.
Figure Shows, we can see that there are a node 3 between 1 ,3,4 and can find out in the animator and there are more than one color ,so according to our algorithm we can find the cut node.
Figure 4.3 Shows, As we mention on the basis of coloring method we can see there is a cut vertices node 3.And if we remove this node we can see node 2 and node 4 and node 1 ,2 will get separated. So node 3 is the cut vertex.
Graph 1. Cost of Connectivity cum Communication: - Graph Shows, The cost of communication explains about the power usage of a device as it is studied that a sensor device does not have the huge power. In this algorithm communication cost decreases efficiently
Graph 2. Time Delay: The time consumption of an algorithm can be computed for an algorithm from starts to end of the algorithm. This procedure continues until it searches out the cut device or vertex. Means the start time is when the sink S sends the probe message and receives the confirm message and this message also specifies that algorithm has initialized and end of algorithm specified when cut node has detected.
VIII. CONCLUSION
Asynchronous distributed algorithm for finding the cut is proposed for WSNs. This algorithm describes a concept based on an coded spanning tree and edge coloring methodology of WSNs. As we have mentioned that CUT may be big anomaly because occurring the CUT separates the network into different parts. Here we finds out the less communication cost and time delay in our proposed algorithm. It also finds robustness in the algorithm. Like after the generating tree, probe and confirm and count messages are sent out.
So it proves also that if the node does not get messages means cut could be occurred somewhere. Optimization strategies to lower the upper bounds of the communication cost and time delay of algorithm are also for future scope and also we may find any of the cut node in the graph because here we are finding the critical node which separates the network so will try to find any of the node.
In this algorithm we are detecting the Cut node with communication cost and time delay.
We can optimize the value of time delay and communication cost, by reducing the upper bound values.
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