Communication cost minimization in wireless

1. Ambitlick Solutions
Communication Cost Minimization in Wireless Sensor and Actor Networks
For Road Surveillance
Objective:
To reduce the communication cost of data transmission in WSANs using Dijkstra’s
algorithm
Abstract:
In recent years, wireless sensor and actor networks (WSANs) have been extensively
deployed to monitor physical environment and facilitate decision making based on data
collected. Emerging applications such as road surveillance highlight some interesting research
issues in WSANs, including coordination problems in sensor–actor or actor–actor
communications, the issue of choosing a set of working actors for coordinating data transmission
in a road sensor and actor network with minimum communication cost. A theoretical model is
introduced to analyze the communication cost of data transmission in WSANs, and the sensor–
actor coordination problem is formulated as an optimization problem. And it can be reduced
using dynamic programming algorithm. A novel graph-based algorithm is also proposed with a
communication-cost graph used to depict the cost of data transmission and a modified Dijkstra’s
algorithm to find optimal solutions in reduced time complexity.
Algorithm Used :
1. dynamic programming algorithm
2. graph-based algorithm
3. Dijkstra’s algorithm

2. Ambitlick Solutions
SYSTEM ANALYSES :
Existing System:
1. Selective communication policies in WSN.
Proposed System:
1. Optimal Selective Forwarding schemes:
• when sensors maximize the importance of their own transmitted messages;
• when sensors maximize the importance of messages that have been successfully
retransmitted by at least one of its neighbors; and
• when sensors maximize the importance of messages that successfully arrive to the sink
2. Introducing the battary power with actual energy , says there is enough energy for a
reasonable number of transmissions. If the node has some battery for only a few transmissions,
the forwarding threshold should start to oscillate and decreases.

3. Ambitlick Solutions
Over all diagram :
Wireless Node Road Surveillance network
Dijkstra’s
Deployment algorithm Inactive Actor
Working actor
Virtual Actor
Energy
Sensors /Actors - detect events
Send their data to nearby actors
Inactive Active Actors
Actors (long-range communications)
Dynamic Programming
Normal Solution
Sensor
Sink

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Data Flow Diagram :
Level 0 :
Wireless Sensor Energy / Batt Sesor / Actor
Node In RSN
Level 1:
Detect Send data to Near-
Dijkstra’s algorithm Actor or Sink
Event
Level 2:
Active long-range Dynamic
sensor communications Programming
Solution
SINK

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Level 3 :
Inactive Act as normal
Sensors sensor
Use case diagram :
Energy/Batt Send data to Near-
Actor or Sink
Active
Actors SINK
long-range
communications
Dynamic Programming
Solution

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Sequence Diagram :
Active Actors Dijkstra’s SINK Sensor
algorithm
Energy/Batt Power
Send data to Near-
Actor or Sink
Inactive sensor /
Actor
Dynamic Programming Solution

7. Ambitlick Solutions
1.Securing Wireless Sensor Networks: A Survey
The significant advances of hardware manufacturing technology and the
development
of efficient software algorithms make technically and economically feasible a network composed
of numerous, small, low-cost sensors using wireless communications, that is, a wireless sensor
network. WSNs have attracted intensive interest from both academia and industry due to their
wide application in civil and military scenarios. In hostile scenarios, it is very important to
protect WSNs from malicious attacks. Due to various resource limitations and the salient features
of a wireless sensor network, the security design for such networks is significantly challenging.
In this article, we present a comprehensive survey of WSN security issues that were investigated
by researchers in recent years and that shed light on future directions for WSN security.
2. On maintaining sensor–actor connectivity in wireless sensor and actor networks:
In wireless sensor and actor networks (WSANs), a group of sensors and actors are
connected by a wireless medium to perform distributed sensing and acting tasks. Sensors usually
gather information in an event area and pass it on to actors, which are resource-rich devices that
make decisions and perform necessary actions. Therefore, it is vital to maintain connections
between sensors and actors for effective sensor- actor coordination. In this paper, we first define
several sensor- actor connection requirements, including weak and strong actor-connectivity, and
then propose several local solutions that put as many sensors as possible to sleep for energy
saving purposes, while meeting different actor-connectivity requirements. We also prove the
relationship between the proposed actor-connectivity and the connectivity in regular graphs,

8. Ambitlick Solutions
which helps with the implementation of the proposed solutions. Comprehensive performance
analysis is conducted through simulations.
3. Communication and coordination in wireless sensor and actor networks:
In this paper, coordination and communication problems in wireless sensor and actor
networks (WSANs) are jointly addressed in a unifying framework. A sensor-actor coordination
model is proposed based on an event-driven partitioning paradigm. Sensors are partitioned into
different sets, and each set is constituted by a data-delivery tree associated with a different actor.
The optimal solution for the partitioning strategy is determined by mathematical programming,
and a distributed solution is proposed. In addition, a new model for the actor-actor coordination
problem is introduced. The actor coordination is formulated as a task assignment optimization
problem for a class of coordination problems in which the area to be acted upon needs to be
optimally split among different actors. An auction-based distributed solution of the problem is
also presented. Performance evaluation shows how global network objectives, such as
compliance with real-time constraints and minimum energy consumption, can be achieved in the
proposed framework with simple interactions between sensors and actors that are suitable for
large-scale networks of energy-constrained devices.
4. Wireless sensor and actor networks: Research challenges
With the maturing of research in wireless sensor networks (WSN) and the more recent
advances in wireless sensor and actor networks (WSAN), there has been an increasing interest in
heterogeneous self-organizing networks with multiple types of nodes that possess different
capabilities and perform diverse tasks in the network's deployment, maintenance, and application
functionalities. This paper explores the conceptual and architectural challenges in the design of

9. Ambitlick Solutions
generic tools for modeling and simulation of such systems. It first addresses the modeling issues,
including the diversity of node types and capabilities, the variety of possible abstractions, and the
need for vertical cross-layer integration. After a brief review of the solutions in some existing
simulation systems, the paper outlines an open architectural platform incorporating the facilities
for: definition of potential capabilities of network elements; formation of node types with
selected capabilities and behavioral algorithms; formation of relevant environment models;
configuration and initialization of the network and its environment; and scenario definition,
execution and monitoring
Modules :
Design Of RSN Network
WIRELESS sensor and actor networks (WSANs), which are composed of a set of sensors
and actors linked by wireless medium to perform distributed sensing and acting tasks. Sensors
are low-cost, low-power devices with limited sensing, computation, and wireless communication
capacities. Actors are assumed to be equipped with better processing capabilities, higher
transmission power, and longer battery life. In WSANs, sensors and actors work together in data-
centric applications, with sensors gathering information about the physical world and actors
taking appropriate actions on the environment
A set of working actors and route sensing data between sensors and actors to minimize
the total communication cost for road surveillance.
Implimentation Of Actors in sensor network

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Sensors detect events and send their data to nearby actors. Unlike other research
assuming actors to be resource-rich nodes with unlimited power supply, we make much weaker
assumptions about actors in our model: We only assume that actors are capable of sensing and
performing long-range communications. That is, in our model, actors are not necessary to be
powerful nodes; they could be resourcelimited nodes operating on batteries, or they could be just
normal sensors that are chosen to collect data and send them to the sink .
Each actor has two states: working or inactive. If an actor is in the working state, it can
sense events, collect data from nearby sensors, and establish long-range communication with the
sink. If it is in the inactive state, it acts like a normal sensor.
Network Communication Cost
In network communication cost: sensor–sensor, sensor–actor, and actor–sink
communication. To simplify the analysis, we assume that the energy cost for unit data
transmission in each hop of sensor–sensor and sensor–actor communication is the same.
Dijkstra's algorithm: From the current intersection, update the distance to every
unvisited intersection that is directly connected to it. This is done by determining the sum of the
distance between an unvisited intersection and the value of the current intersection, and
relabeling the unvisited intersection with this value if it is less than its current value. After you
have updated the distances to each neighboring intersection, mark the current intersection as
visited and select the unvisited intersection with lowest distance.

11. Ambitlick Solutions
Dynamic Programming Solution:
When the system is in idle condition, the OPT algorithm turns off as many actors as
possible to save energy. For normal road surveillance, it yields the lowest energy cost, and its
performance is insensitive to actor density. In a busy traffic environment, it keeps the workload
of working actors at a low level.
The dynamic programming algorithm produce an optimal solution based on the
assumption of virtual working actors. In real RSANs, working actors may be not deployed in
each intersection; thus, the solution may be not optimal in the real case. The following theorem
shows that the proposed algorithms produce a near-optimal solution when there are no working
actors in the intersections.
Performance Evaluvation :
a. Energy Vs Node Density
b. No.Of.Worling Actors Vs Node Density
c. Energy Vs Actor Density
d. No.Of.Worling Actors Vs Actor Density
e. Communication Overhead Vs No Of Sensor Nodes

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REFERENCES
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1524–1538, Nov. 2009.
[2] R. Arroyo-Valles, A. G. Marques, and J. Cid-Sueiro, Wireless Sensor Networks. IN-TECH,
2010, ch. Energy-aware Selective Communications in Sensor Networks.
[3] E. Shih, S.-H. Cho, N. Ickes, R. Min, A. Sinha, A. Wang, and A. Chandrakasan, “Physical
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