Abstract: Connected vehicles are equipped with wireless sensors
that aid in Vehicle to Vehicle (V2V) and Vehicle to Infrastructure
(V2I) communication. These vehicles will in the near future
provide road safety, improve transport efficiency, and reduce traffic
congestion. One of the challenges for connected vehicles is how
to ensure that information sent across the network is secure. If
security of the network is not guaranteed, several attacks can occur,
thereby compromising the robustness, reliability, and efficiency of
the network. This paper discusses existing security mechanisms and
unique properties of connected vehicles. The methodology employed
in this work is exploratory. The paper reviews existing security
solutions for connected vehicles. More concretely, it discusses
various cryptographic mechanisms available, and suggests areas
of improvement. The study proposes a combination of symmetric
key encryption and public key cryptography to improve security.
The study further proposes message aggregation as a technique to
overcome message redundancy. This paper offers a comprehensive
overview of connected vehicles technology, its applications, its
security mechanisms, open challenges, and potential areas of future
research.
Abstract: In wireless sensor network, sensor node transmits the
sensed data to the sink node in multi-hop communication
periodically. This high traffic induces congestion at the node which is
present one-hop distance to the sink node. The packet transmission
and reception rate of these nodes should be very high, when
compared to other sensor nodes in the network. Therefore, the energy
consumption of that node is very high and this effect is known as the
“funneling effect”. The tree based-data aggregation technique
(TBDA) is used to reduce the energy consumption of the node. The
throughput of the overall performance shows a considerable decrease
in the number of packet transmissions to the sink node. The proposed
scheme, TBDA, avoids the funneling effect and extends the lifetime
of the wireless sensor network. The average case time complexity for
inserting the node in the tree is O(n log n) and for the worst case time
complexity is O(n2).
Abstract: Optimization plays an important role in most real
world applications that support decision makers to take the right
decision regarding the strategic directions and operations of the
system they manage. Solutions for traffic management and traffic
congestion problems are considered major problems that most
decision making authorities for cities around the world are looking
for. This review paper gives a full description of the traffic problem
as part of the transportation planning process and present a view as a
framework of urban transportation system analysis where the core of
the system is a transportation network equilibrium model that is
based on optimization techniques and that can also be used for
evaluating an alternative solution or a combination of alternative
solutions for the traffic congestion. Different transportation network
equilibrium models are reviewed from the sequential approach to the
multiclass combining trip generation, trip distribution, modal split,
trip assignment and departure time model. A GIS-Based intelligent
decision support system framework for urban transportation system
analysis is suggested for implementation where the selection of
optimized alternative solutions, single or packages, will be based on
an intelligent agent rather than human being which would lead to
reduction in time, cost and the elimination of the difficulty, by
human being, for finding the best solution to the traffic congestion
problem.