இன்டர்நேஷனல் ஜர்னல் ஆஃப் சென்சார் நெட்வொர்க்ஸ் மற்றும் டேட்டா கம்யூனிகேஷன்ஸ்

In this paper we propose the use of a malleable signature technique to transform Brands’ digital credentials from
single-show capability to multi-show capability. Our specific proposed instantiation uses RSA digital signatures so that
Alice can efficiently transform an original credential and its corresponding CA signature to a randomized / blinded
version of the credential and signature that can be shown to a verifier without risk of traceability (by the CA or across
multiple verifiers). We describe our modified issuing and showing protocols and discuss the security properties of our
proposed scheme.

Wireless data communication between downhole equipment and surface platform in oil wells is of high importance for
successful and cost effective drilling operations. In this letter two types of sensors, an accelerometer and a strain sensor
are considered as receivers of modulated acoustic signals propagated through steel pipes of drill strings in oil wells.
An accelerometer measures particle acceleration whereas a strain sensor measures fractional particle displacement.
Using measured channel impulse responses, two important characteristics of wireless acceleration and strain channels,
delay spreads and eigenvalues are studied. The experimental results show that strain channels exhibit smaller delay
spreads and larger eigenvalues. Therefore, they can provide lower bit error probabilities and better communication
performance for data transmission and telemetry. The results are useful for implementing improved systems for wireless
data communication through drill strings in oil wells.

Agriculture is the backbone of a country particularly in South Asia because of excessive agrarian land resides there.
It is beneficial for human growth and the State’s emerging Gross Domestic Product (GDP). South Asia particularly
Pakistan is facing severs challenges of poverty, hunger and malnutrition along with growing population. Under these
conditions, horticulture governed with emerging scientific research and developments can play a vital role in order to
cope up with such challenges. Tunnel farming is such kind of one development being practiced across the globe to
obtain off-season crops with three folds of more production as compared to the traditional way of vegetation. In this
regard the concept of Internet of Things (IoT) is a promising approach which finds its expertise in every field of life
including agriculture sector. Its application in tunnel farming has been generating ambitious results in the past few
years therefore, a sound architecture and model based on latest, robust and reliable technologies can change the fate
of agricultural sector in Pakistan. This paper proposed a framework with hardware design which comprises of accurate
sensors for data transmission to Cortex based MPU, cloud based data unit and further appropriate actuators to regulate
the desired microclimatic conditions inside the tunnel to improve the productivity followed by more revenue. Proposed
hardware model provides a cheap, optimal and automatic tunnel farming solution to the middle class farmer, growing
off-season crops at the far ends of the country with scarcity of electricity and water resources. Furthermore it presents a
reliable user friendly database and a very simple GUI that makes it easy for the farmer to monitor the climatic changes,
optimal resource utilization, the production at the far site end and its healthy transportation to the nearest consumer
market along with periodic reporting of location and vital microclimatic conditions inside the vehicle. To our knowledge,
this is the first proposed hardware model which uses Cortex based microcontroller for Smart Tunnel Farming. By the
proposed hardware model, the cost of electricity and water can be reduced by 30 to 40% and an average of 20%
increment in the production and supply of targeted crops by automatic adjustment of optimal climatic conditions inside
the tunnel and monitoring of crops transportation system.

Since the adoption of Bluetooth Low Energy (BLE) in the Bluetooth standard in 2010, BLE beacons are emerging as
one of the most viable solutions for indoor localization due to its power efficient architecture, short scan duration, low cost
chipset, and wide adoption in the devices. The existing indoor positioning systems based on BLE beacons employ the
classical fingerprinting (FP) technique where user terminals collect signals from the beacons and do most of localization
computations, requiring significant power consumption on user devices. However, constant power consumption
on limited battery life of a mobile device can be problematic when it comes to supporting server-oriented tracking
applications. To address this issue, we have proposed a new fingerprinting technique called inverse fingerprinting
(Inv-FP), which is a server side BLE fingerprint system where most of the positioning computations are done by BLE
sniffers and servers, thus minimizing the computation overhead of user devices. However, the absolute positioning
schemes such as FP and Inv-FP do not use the current position estimate to determine the next position. This leads to
discontiguous, irregular route prediction especially when the positioning accuracy is low, since it does not reflect the
continuity of the position change according to the movement of the user. In contrast, a relative positioning scheme such
as Pedestrian Dead Reckoning (PDR) determines the current position based on the previous position, reflecting the
continuity of the position change but it cannot estimate the current position without the initial position. In this paper, we
implement both FP and Inv-FP and evaluate their performance in small and large-scale testbeds. We analyze various
characteristics of Inv-FP in comparison with the classical beacon based FP, and demonstrate that Inv-FP can match
the performance of FP but with minimal power consumption on user devices. In addition, we propose a new localization
algorithm that can combine Inv-FP with PDR. By integrating PDR with Inv-FP, we show that localization error can be
reduced by reflecting the advantages of each method.

Air pollution poses a severe threat to humans and the natural environment. High levels of air pollution are linked to respiratory problems, mental illness, skin diseases etc. Monitoring air pollution to report, study and take appropriate actions to curb it is an important agenda on many civic and government institutions. This work aims to develop a capable system to cover these grounds. The system is cost effective, portable, and accurate for providing warnings in case life threatening levels of pollutants is discovered. These specifications were responsible in the selection of ATmega32 processor with Arduino UNO development board for the main air pollution monitoring unit. It is a low power controller and can be powered by solar panels. The sensors are capable of detecting CO, CO2, Methane, dust of PM 1.0, PM 10.0 and temperature and humidity are passed through a conditioning circuit and are then interfaced to the main control unit. The signals are calibrated initially to reduce erroneous detections. Short messaging service or SMS is used to wirelessly transmit data to a base station which is capable of alerting in case of a hazard. Wireless networks eliminate many limitations posed by wired networks, mainly costs related to wiring infrastructure.