In this paper, a strip-shaped multi-hop ad hoc network is analyzed
using a spatial Poisson pointprocess (PPP) and stochastic geometry. The
decode-and-forwardprotocol is considered for transmission overthe
multi-hop network where cooperative communications is employed at each
hop. An analytical expressionfor the probability density function of the
received power at an arbitrary node is derived, given a set of
nodestransmits in the previous hop, which is further used to
characterize the coverage performance of the network.The received power
at a node becomes a doubly stochastic process owing to random path loss
and a Rayleighfading channel. The notions of one-hop success probability
and coverage range are analyzed for variousnetwork parameters. An
algorithm for conserving energy is also proposed by considering PPP
thinning andits performance in terms of the fraction of energy saved is
quantified. It is shown that the proposed algorithmis more energy
efficient as compared with an independent thinning algorithm.
Future wireless networks pose critical challenges in terms
ofreliability and seamless coverage. Whereas future networkswill be an
amalgamation of sophisticated techniques underthe umbrella of fifth
generation (5G), an integral part of5G communications will largely be
composed of Internet ofThings (IoT).
Sensor and ad hoc networks constitute a majorportion of IoT and
communications between various entitiesof these networks plays a vital
role for their successful oper-ation. Cooperative transmission (CT) is
one of the relayingtechniques for wireless sensor and ad hoc networks
usedprimarily to enhance the reliability of the received signals.The
nodes cooperate to form a virtual antenna array andtransmit the same
signal towards the nodes of the next level orhop, thereby providing
spatial diversity gain [1]. A CT multi-hop mechanism provides an
efficient method for reaching adistant destination as the transmit
powers of the nodes can bereduced without compromising the reliability
[2].Opportunistic Large Array (OLA) is a form of physicallayer CT [3],
where multiple nodes in a hop transmit the samemessage, without any
coordination among each other andwithout any addressing scheme. A
promising characteristicof this technique is that it does not require
any prior infor-mation of the number of cooperating nodes or their
locations,which makes it scalable and suitable for transmission
withoutany cluster head. In an OLA transmission, a source nodebroadcasts
its message and all nodes in the vicinity thatcan decode this message,
become part of level 1, which areknown as decode-and-forward (DF) nodes.
In the next timeslot, these DF nodes transmit the same message
concurrentlyin the forward direction using cooperation and the
processcontinues until the data is reached at the destination or
broad-casted to the entire network. However, the modeling of
signaltransmission over this multi-hop network is not very
straightforward and the foremost model was proposed in [4].The proposed
model for a strip-shaped OLA networkin [4] assumes infinite number of
nodes transmitting con-stant power per unit area, which guaranteed
infinite signalpropagation over the network. This assumption
ofcontinuumof nodes confined its application to networks with very high
node density. However, it was shown in [5] that a finitenode density
cannot lead to an infinite broadcast and thatthe path loss exponent
plays a major role in controlling thebroadcast region. Hassan and Ingram
[6] studied the OLAline network with finite node density and modeled
the nodelocations with Bernoulli point process. This model is
thenextended in [7] to a strip-shaped network with deterministichop
boundaries. Specifically, the authors model an ad hocnetwork where the
number of nodes in each hop is knowna priori. Moreover, fixed hop
boundaries were assumed and aMarkov chain model was derived to study the
characteristicsof multi-hop transmissions over the network. We extend
themodel in [8] with random number of nodes per hop and fixedhop
boundaries.In this paper, we study the coverage of a more generalsetup,
where the number of nodes per hop as well as hopboundaries are kept
random. The transmission model resem-bles a typical OLA, where the
transmission of the signal froma source to a distant destination forms
irregular levels or hopswith random number of nodes in each hop. We
derive thecoverage probability of this network using the distributionof
the received power at a node, which is subject to channelimpairments
that include independent Rayleigh fading andpath loss.Our stochastic
model is based on the theory of Poissonpoint process (PPP) [9], where
the nodes in each hop areindependent and distributed according to a
Poisson randomvariable (RV). The analytical tractability of the PPP
modelmakes it a suitable candidate to analyze the random numberof nodes
in each hop; unlike fixed number of nodes, whichcan be generally modeled
using Markov chains [10]. Oncemodeled, the void probability of PPP is
used to computevarious network performance metrics such asm-hop
successprobability, coverage range (CR) and required node densityto
achieve a particular CR under a quality of service (QoS)constraint.The
proposed stochastic model helps in determining theCR of a pure OLA
network given the node density of thenetwork for various hop distances.
It provides useful insightsin designing a network in terms of one-hop
success prob-ability,m-hop success probability and fraction of
energysaved (FES). Its applications include, but not limited to,
smartgrid communication system or fault recognition system
fortransmission lines, and structural health monitoring systemfor the
overhead bridges and tunnels [11]. The model andits findings can also be
used to set up an inter-vehicularcommunication system on the motorways
[12] or a vehicularad hoc network (VANET) to monitor highway activity
bydistributing the nodes along the highway.The geometric complexity of
the system increases withrandom node locations and irregular hop
boundaries, mak-ing path loss a random process. The path loss is
dependentupon the Euclidean distance between the nodes, which
whencombined with fading provides the notion of signal-to-noiseratio
(SNR) for a single link. In CT, multiple single-inputsingle-output
(SISO) links are averaged over a PPP to analyzevirtual multiple-input
single-output (MISO) links. Our maincontributions in this paper are the
followings.•Derivation of the distribution of the Euclidean
distancebetween a pair of nodes distributed randomly in
adjacentoverlapping levels without any hypothetical boundary inbetween
them.•It is shown with the help of some statistical approachessuch as
the moment matching method that the distribu-tion of the distance raised
to a positive power can be wellapproximated by a Weibull
distribution.•We derive the distribution of the received power for
avirtual MISO link, which is the random sum over a PPPof the ratio of an
exponential random variable (RV) anda Weibull RV.•We derive the
coverage range of a 2-dimensional (2D)strip-shaped multiple hop OLA
network.•We devise a thinning of PPP to conserve energy for thefinite
node density OLA networks with random nodeplacements by allowing only a
subset of nodes to trans-mit and quantify its performance. View More
Performance in Android Devices Market share for android smartphones have been increasing exponentially day by day. With this increase in numbers of phones sold every year, even the demands for a better quality of the android phones are increasing in the competitive market. To meet the high expectations of users, companies are putting good amount of efforts in the improvement of hardware as well as software. While smartphone hardware market is already saturating to an extent, there is a lot of scope for improvement on software design on top of Google’s stock Android for better user experience. As far as user experience is concerned, application launch time is one of the most important performance parameter for any smartphone device. In general, due to the usage of many applications, a good amount of RAM gets consumed resulting in phone sluggishness. This sluggishness is very much visible to the user especially when a particular application, which user wanted to re-use, is already killed by LMK. In our proposed solution, we intend to provide a better user experience by improving LMK’s algorithm based on user’s usage of various applications. Thereby, enhance user experience by decreasing application launch time of favorite application Android operating system has an in-built task killer, called low memory killer (LMK). The LMK keeps an eye on the real time RAM usage of all applications. Whenever excess of RAM is consumed, the LMK starts killing applications to free-upsome memory [4][5]. For the killing of applications, LMK has defined some set of priorities through oom_adj value. This oom_adj is set by android kernel for each process on the Android system. It ranges from -17 at the highest to +15 at the lowest. Therefore, in memory crunch situation, LMK is called and it starts killing applications from the lowest oom_adj i.e. oom_adj value of +15. For this paper, we target only the cached applications that range from +9 to +15 [5]. As important applications and services have higher priority than cached applications, so we need not worry about them.https://codeshoppy.in/ In general scenario, user opens few applications frequently than others. Both of our proposed solutions decrease the probability of killing such important user applications by LMK in memory crunch scenarios. In our proposed solution, we have added more parameter checks to LMK algorithm to make sure killing of applications occur not only based on LRU list but also based on the importance of the application to the user. Given below are the two new approaches:
In order to avoid heavy applications falling in this category, we also keep a check for the memory proportionate set size (PSS) value [7]. Whenever the PSS value goes higher than 30 MB we don’t consider applying our algorithm to such applications. We have included this check in our algorithm in order to remove any possibility that may hamper our algorithm due to memory leak of any frequently used heavy application. As process record gets update many a times, these checks are put in the right location which doesn’t increase the overhead of calculation very much.
Android Application for Healthy Food Recently, there are many people who ignore health concerns especially in their eating habits. This has made the number of diseases found in society, especially noncommunicable diseases (NCDs) such as diabetes, hypertension, and cancer drastically increase every year. The number of patients having these diseases, could be reduced by paying more attention to the food that they eat and nutrition that they receive. Accordingly, the researchers would like to propose FoodForCare, an Android application for self-care with healthy food. The main purpose is to help users have better eating habits and a healthier lifestyle. FoodForCare provides functions for users to keep their daily personal health and food records of food intake. The users can see an analysis of nutrition and calories per day and whether it is sufficient or not. This application can give an overview on food calories and nutrition so that they can eat wisely. Finally, the development of this application hopes to help Thai people in order to manage their total nutrition and calories taken for a healthier lifestyle and will directly decrease the number of people who are getting diseases caused from a disorder of food and nutrition. Self-Care by Healthy Food and Weight Control Self-care [11] is an act when people intend to take care of themselves with respect to physical, mental and emotional health. They may use self-care to prevent themselves from illnesses, try to lose weight, or challenge their self to be healthier. The researchers are focusing on Self-care with nutritional food. Food is the topic that people often overlook, they are too busy or do not have time to consider eating healthy and nutritional food. They usually substitute it with fast food or a snack loaded with sugar instead of a regular nutritional meal. The lack of adequate nutrition will cause people to encounter with many diseasesPeople can easily self-check their body measurement by having weight control, which can be defined by Body Mass Index (BMI) [12], Basal Metabolic Rate (BMR) [13], and Total Daily Energy Expenditure (TDEE) [14]. All of these can be calculated by weight, height, age, gender and the daily activities of that person. It is essential to know the overall body status to be aware of health conditions and body metabolisSurvey existing applications A number of existing applications related to food, diet and calories control were explored. Nine interesting applications closely related to the development of FoodForCare are briefly discussed as follows. Table I displays feature comparison of the nine applications with FoodForCare which are CalTracker[ 15], Calories Diary [16], F oodiEat[ 17], Kcal Check Calories[18], Calories Counter[19], Low Calories Recipe[20], Food That Help Your Body Heal[21], My Diet Diary[22], and Clean Food[23Most of the applications have only some basic features such as record, calculate and analyze calories in each day. However, all applications surveyed still had some weak points. Many applications do not provide nutritional information, clean food and a food guide. It would be more convenient for users to use one application like FoodForCare that can provide all features CodeShoppy
The FoodForCare application requires the user to register as a member by giving their personal information and log-in to use the application. The application has seven main features, as described below: /) Profile -This feature enables users to manage the profile that is stored on the application and see recorded weight history. 2) My Diary -This feature helps users record daily food intake and allows users to see analysis of monthly calories consumed and daily nutrition gained compared with standards. 3) Food List -This feature provides users with the information of each food menu with kcal, proteins, carbohydrates, and fats in various type of categories such as healthy food, normal food and food for diabetic, hypertension and cancer patients. 4) Food Guide -This feature supplies users with informative tips that user can be used to gain benefits. 5) Body Measurement -This feature helps users track the measurement of the body from weight, height, age, gender, and activities with BMI, BMR and TDEE calculation. 6) Notification -This feature enables users to set the date and time as a reminder to note the time to record food intake. Challenge -This feature allows users to challenge themselves to be healthy by using the recorded data that users note over a 7-days calories controlled period. Users who can accomplish the challenge could share the success on Facebook.
