- Research
- Open Access
CGA-based deadlock solving strategies towards vehicle sensing systems
- Jianlong Xu^{1},
- Zibin Zheng^{1}Email author and
- Michael R Lyu^{1}
https://doi.org/10.1186/1687-1499-2014-214
© Xu et al.; licensee Springer. 2014
- Received: 9 September 2014
- Accepted: 24 November 2014
- Published: 6 December 2014
Abstract
Vehicle sensing system is an important research topic in the research field of Internet-of-Vehicles (IoV). Reliability and real-time performance of vehicle sensing systems are greatly influenced when deadlock happens. When a deadlock is detected, identifying the optimal deadlock solving strategy can ensure that the system goes back to normal state quickly. In order to address this issue, this paper proposes an efficient deadlock solving method. Firstly, the deadlock problem in a vehicle sensing system is analyzed based on four deadlock occurring conditions (i.e., mutual exclusion, hold and wait, no preemption, and circular wait). Secondly, an optimization model is built to combine the quantity and cost of tasks in vehicle sensing systems. After that, a co-evolutionary genetic algorithm (CGA) is developed to search the optimal deadlock solving strategy. Finally, experiments by simulation are conducted and the experimental results show the efficiency of the proposed deadlock solving method for vehicle sensing systems.
Keywords
- Vehicle sensing system
- Deadlock solving
- Co-evolutionary genetic algorithm
1 Introduction
With the development of intelligent transport systems (ITSs), Internet-of-Vehicles (IoV) techniques are booming rapidly in recent years. IoV enables information sharing and gathering on vehicles, roads, and their surrounds. Building highly efficient and intelligent vehicles is very important for automobile industry [1, 2]. Vehicle sensing systems, which are important research topics in IoV, have attracted high attentions of many research institutions worldwide [3, 4]. Vehicles are equipped with hundreds of intelligent sensing devices, such as GPS-based sensors, video cameras, and other physical and chemical sensors, which are distributed over their chassis, powertrain, body, wheel areas, and so on [5]. Some sensors are independent, while others communicate with each other through wired or wireless networks, such as controller area network (CAN), Bluetooth, ZigBee, LowPan, etc. These sensing devices form a complex heterogeneous network inside the vehicle. They are capable of collecting, processing, storing, and transferring information from one node to another, and they perform a lot of tasks, including monitoring temperature, pressure, humidity, vehicular motion state, noise levels, and so on. Generally, new and pertinent information will be obtained by collecting and processing the raw sensor data. For example, three parameters including motion attitude, dynamic load, and braking performance can be obtained by utilizing data from acceleration sensors, temperature sensors, pressure sensors, and GPS-based sensors, which are embedded in the vehicle wheel, body, and suspension mechanism [6, 7]. In these cases, the collecting and processing tasks which request resources (e.g. communication resource, storage resource, etc.) must wait until they acquire all the requested resources. If the requested resources are held by other tasks, deadlocks may happen in the system, which will greatly influence the system reliability and real-time performance. Therefore, the deadlock problems must be taken into consideration in the design and development phases of vehicle sensing systems.
In vehicle sensing systems, because of the variety of tasks and resources, the deadlock is hard to be predicted. The efficiency of existing deadlock solving methods needs to be improved. Different from traditional software systems, vehicle sensing systems are safety critical, where deadlock problems may lead to serious and fatal consequences. Moreover, vehicle sensing systems have a number of sensors, which are shared among different tasks, making deadlock problems more notable. Existing work [8] has mentioned the idea of enhancing the quality of hardware to improve the safety and reliability of vehicles. However, this hardware-based approach is expensive and not practical. Therefore, in order to find better ways to make sure that the system is more stable and reliable, more practical solutions are urgently needed for vehicle sensing systems.
In order to address this critical challenge, this paper proposes an efficient deadlock solving method, which aims at improving the efficiency of deadlock solving mechanisms in vehicle sensing systems. In summary, the major contributions of this paper include: 1) we analyze the deadlock problem in vehicle sensing systems based on the deadlock occurring conditions; and 2) we build a deadlock solving optimization model and propose a deadlock solving method based on co-evolutionary genetic algorithm for vehicle sensing systems, which can quickly solve the deadlock problem and ensure the minimum cost.
The rest of this paper is organized as follows: Section 2 summarizes related work on vehicle sensing systems and deadlock solving method. Section 3 explains the deadlock analysis model in the vehicle sensing systems. Section 4 first describes the deadlock solving optimization problem and then proposes an optimization method based on co-evolutionary genetic algorithm to search the optimal deadlock solving strategy. Afterwards, Section 5 demonstrates the experimental results. Finally, Section 6 concludes the paper and discusses the future work.
2 Related work
2.1 Vehicle sensing systems
Vehicle sensing systems have been one of the most active research areas in the automotive field over the past and current decades [5]. In terms of safety, pollutants, greenhouse gases emissions, comfort and energy consumption, etc., research in vehicle sensing systems is conducted from various aspects, aiming to improve the system reliability. Yimetal [8] proposed a Smart Car Sensor Network (SCSN) to improve the communication reliability for sensor networks. Taghvaeeyan et al. [9] proposed position estimation algorithms based on the use of anisotropic magnetoresistive sensors for imminent crash detection in cars. In the work of [10], Taghvaeeyan further proposed a two-dimensional sensor system for automotive crash prediction. Georgakosetal [11] also discussed the reliability challenges for electric vehicles from devices to architecture and systems software. Xiuying et al. [12] focused on a velocity monitoring system based on the multi-sensor data fusion method, which was applied in car navigation and positioning systems. With the development of vehicle sensing technology, reliability of the vehicle sensing system is becoming more and more important.
2.2 Deadlock solving method
Deadlock is one of the most important problems in both centralized and distributed systems. A deadlock is a situation in which two or more competing actions are waiting for each other to finish, and thus neither ever does [13, 14].
Deadlock problem solving strategies can be classified into active strategies and passive strategies. Active strategies solve a deadlock before it occurs by online or offline methods. Active strategies include deadlock prevention strategy and deadlock avoiding strategy. The main purpose of deadlock prevention strategy is to ensure that the system always stays away from a deadlock state. It uses offline calculation mechanism to control the request and allocation of resource through imposing restrictions to the system. Various deadlock prevention strategies are proposed in recent years. Yi-Sheng et al. [15] proposed a deadlock prevention algorithm for sequence resource allocation systems, employing Petri nets to build and describe the systems of simple sequential the processes with resources (S ^{3}PGR) model. On the other hand, for the deadlock avoiding strategy, system state is monitored continuously in the process of system operation. The system scheduling policy will judge whether a process will lead to a deadlock or not and then decides the next system operation process. Ballaletal [16] proposed the MAXWIP (Max a work in progress) algorithm for deadlock avoidance in mobile wireless sensor network monitoring systems, which was described as Free Choice Multi-Reentrant Flow Line (FMRF) systems.The advantage of this method is that system operating efficiency can be kept in the greatest degree. But it must obtain all the system reachable states first, which is not realistic in medium- or large-scale systems.
Passive strategies solve the deadlock problem when a deadlock occurs, including deadlock detection strategy and deadlock relieving strategy. The efficiency of passive strategies depends on the speed of deadlock detection and deadlock relieving. After a deadlock is detected, the system can be unlocked by automatic or manual methods. While this approach tends to achieve high real-time performance and resource utilization, it requires full understanding of the deadlock. Abd El-Gwad et al. [17] proposed a deadlock detection protocol based on threads, which schedules the threads in order to detect which thread would initiate the deadlock. Aydin Aybar et al. [18] presented an approach to design a supervisory controller for a timed Petri nets (TPNs) to avoid deadlock by using the method of stretching. The presented approach determines the least restrictive controller which guarantees deadlock avoidance, whenever such a controller exists. Steghofer et al. [19] described a distributed deadlock avoidance algorithm for self-organizing resource flow systems. The algorithm leverages implicit local knowledge about the system structure and uses a simple coordination mechanism to detect loops in the resource flow. But this algorithm must use specific system architecture knowledge and its generality is not strong. Xing et al. [20] proposed a deadlock-free genetic scheduling algorithm to optimize the performance of automated manufacturing systems based on deadlock control policy, which embedded the optimal deadlock avoidance policy into the genetic algorithm and used the one-step look-ahead method in the optimal deadlock control policy.
In the above research investigations, researchers focus on vehicle sensing systems or deadlock solving methods respectively. There is limited study on the deadlock problems in vehicle sensing systems. In order to improve the system reliability, we propose a deadlock solving method for vehicle sensing systems in this paper.
3 Vehicle sensing system deadlock analysis
In Equation 1, V includes T and R and set E is constituted by all the ordered pair (T_{ i },R_{ j }) and (R_{ j },T_{ i }).
More specifically, V={T_{1},T_{2},…,T_{ n },R_{1},R_{2},…,R_{ m }}, E={(T_{ i },R_{ j }),(R_{ i },T_{ j })|T_{ i }∈T,R_{ j }∈R}. If {(T_{ i },R_{ j })}∈E, it expresses that there is a directed edge from T_{ i } to R_{ j }, which means task T_{ i } requests resource R_{ j }. If {(R_{ j },T_{ i })}∈E, then the directed edge is from R_{ j } to R_{ i }, which means R_{ j } is held by T_{ i }.
Previous research indicates that four conditions may lead to a deadlock, which are mutual exclusion, hold and wait, no preemption, and circular wait[13, 21]. In vehicle sensing systems, corresponding deadlock conditions are described as follows.
Mutual exclusion is expressed as $\left(\exists \left({R}_{j},{T}_{i}\right)\in E\right)\wedge \left(\nexists \left({R}_{j},{T}_{l}\right)\in E\right),l\ne i$, which means that system resource R_{ j } can only be held by a task (e.g., sensor information collection task, etc.) or keep idle; other tasks cannot hold this resource at the same time.
Hold and wait can be expressed as ((R_{ j },T_{ i })∈E)∧((T_{ i },R_{ l })∈E)∧((R_{ l },T_{ l })∈E),l≠i,which means T_{ i } has held at least one resource R_{ j } and requested a new resource R_{ l }, but R_{ l } has been held by T_{ l }. The request will be blocked because the resource R_{ l } has not been released.
No preemption is expressed as (t<t_{ i })∧(∃(R_{ j },T_{ l })∈E). t_{ i } is the time of T_{ i } holds R_{ j }. It means sensing resources R_{ j } cannot be held or deprived by other tasks before task T_{ i } releases it.
Circular wait can be expressed as ∃{T={T_{1},T_{2},…,T_{ n }},R={R_{1},R_{2},…,R_{ m }}},∃((R_{1},T_{1})∈E))∧((R_{2},T_{2})∈E)∧…((R_{ n },R_{ n })∈E) and ∃((T_{1},R_{2})∈E))∧((T_{2},R_{3})∈E)∧…((T_{ n },R_{1})∈E). It means that T_{1} is waiting for R_{2} which is held by T_{2}, T_{2} is waiting for R_{3} which is held by T_{3},…, and T_{ n } is waiting for R_{1} which is held by T_{1}. Thus, it results in a circular chain.
4 Deadlock solving optimization method based on co-evolutionary genetic algorithm
When a deadlock is detected, a deadlock solving mechanism is needed to ensure that the system goes back to normal. Generally, the method of solving deadlock is to abort one or more tasks and consequently release the held resources, which can break the state of circular wait. This section first describes the deadlock solving optimization problem and then proposes an optimization method based on co-evolutionary genetic algorithm.
4.1 Deadlock solving optimization problem of vehicle sensing systems
In order to break the deadlock, it is necessary to release HR &RR held by some tasks. When deadlock happens and the location is unknown, N_{ j } resources are involved, where some of them need to be reallocated. It is complex to decide which resources should be released from the tasks. So, the deadlock solving strategy is not unique.
Seven strategies for deadlock solving
Strategies | Can solve the deadlock or not? |
---|---|
Abort T_{1} | No |
Abort T_{2} | No |
Abort T_{3} | Yes |
Abort T_{1} and T_{2} | Yes |
Abort T_{2} and T_{3} | Yes |
Abort T_{1} and T_{3} | Yes |
Abort T_{1},T_{2}, and T_{3} | Yes |
Obviously, the deadlock solving strategy is not unique, and the optimal strategy is the one that aborts the least number of tasks. However, other factors (e.g., cost, operating state, urgency degree, priority, etc.) may be different with each task. For example, if the costs of aborting T_{1}, T_{2}, and T_{3} are 2, 3, and 4, respectively, then the cost of aborting T_{1} and T_{2} is 5, but the cost of aborting T_{3} is 4, so the latter is a better strategy. In this paper, we try to identify the optimal deadline solving strategy considering both quantity of aborted tasks and their cost.
In Equation 3: ${N}_{{T}_{i}\leftarrow {R}_{j}}$ denotes the number of resource R_{ j } held by task T_{ i }, ${N}_{{T}_{i}\to {R}_{j}}$ denotes the number of resource R_{ j } requested by task T_{ i }, N_{ j } is the number of the same kinds of factors in R_{ j }, cost _{ i } is the cost of aborting the task T_{ i }, S_{ i } is the state of T_{ i }, S_{ i }∈{0,1}. We assume 0 stands for aborting a task while 1 stands for not aborting a task. For this model, there are 2n−1 kinds of possible strategies which need to be checked. Some strategies can solve the deadlock problem while others cannot. Since the optimal solution search space is very large, it is time consuming to obtain the optimal solution using an exhaustive method. To address this problem, we propose a deadlock solving method based on a genetic algorithm to enhance the efficiency.
4.2 Deadlock solving optimization method based on co-evolutionary genetic algorithm
Genetic algorithm can search through simulating natural selection and evolution, and they enjoy excellent global optimal convergence ability [22]. It uses basic operators, namely crossover and mutation, and is based on the evolution of a population of similar individuals (species from the same class) [23]. Each individual can be a solution for the target problem we want to solve. Basic genetic algorithm has been a good way of solving many optimization problems and got comparative desirable results. However, it also has some shortcomings in some cases which are intrinsically distributed. In other words, it can be seen as several independent entities with their own goals interacting with each other, which may go against the whole goal of solution. To address these kinds of problems, in this paper, we employ co-evolutionary genetic algorithm to solve the deadlock strategies choices. In our deadlock solving optimization method, each deadlock solving strategies are considered as individuals. The population, namely, a collection of such individuals, will encounter genetic operators such as crossover and mutation in each generation. After a number of iterations, the best individual obtained is the best deadlock strategy.
4.2.1 Outline of the co-evolutionary genetic algorithm
4.2.2 Deadlock solving optimization in GA-H
In Equation 4, V_{ i }(k) represents the individual after k th evolution; cost _{ i } is the cost of task T_{ i }; C(V_{ i }(k)) is the cost of deadlock solving strategy.
Individuals with higher fitness will have greater probability to be selected. Fitness function will accelerate convergence speed of the algorithm and avoid the algorithm from being trapped into a local optimum.
In the genetic algorithm, after offspring crossover and mutation, parent and offspring individuals will compete into the next population and to be selected according to the fitness. Two selected parent individuals may change information according to crossover probability P_{ c } which depends on whether the genes are different in the same location. The individuals may mutate the same genes according to mutation probability P_{ m }.
4.2.3 Deadlock solving optimization in GA-P
where, ${\nu}_{{i}_{l}}$denotes the fitness value of the i_{ l } individual in the GA-H.
where ν_{max} and ν_{min} denote maximum and minimum fitness values in GA-H, respectively, and hence P is a value such that 0<P<1.
By applying this optimization method, we can search the best deadlock solution.
5 Experiment
In this section, we conduct experiments to validate our approaches using simulations. Our experiments are intended to: 1) verify the rationality of our proposed deadlock solving method; and 2) discuss how the model parameters affect the results. All the experiments in this paper are based on Intel CORE-i7 (2.9 GHz) machine with 8 GB RAM.
5.1 Experimental settings
Cost of each task and the number of each resource
Tasks | Cost | Resources | Number |
---|---|---|---|
T _{1} | 10 | R _{1} | 6 |
T _{2} | 9 | R _{2} | 6 |
T _{3} | 8 | R _{3} | 8 |
T _{4} | 7 | R _{4} | 6 |
T _{5} | 6 | R _{5} | 4 |
T _{6} | 5 | R _{6} | 8 |
T _{7} | 4 | ||
T _{8} | 3 |
A situation of resource allocation according to each task
Resources | R _{1} | R _{2} | R _{3} | R _{4} | R _{5} | R _{6} |
---|---|---|---|---|---|---|
Task | ||||||
T _{1} | 2/0 | 0/3 | 2/0 | 0/0 | 1/0 | 0/0 |
T _{2} | 0/0 | 3/0 | 2/0 | 0/1 | 0/0 | 0/0 |
T _{3} | 0/0 | 0/2 | 0/0 | 2/0 | 3/0 | 0/0 |
T _{4} | 1/0 | 0/0 | 1/0 | 0/3 | 0/0 | 4/0 |
T _{5} | 0/0 | 0/2 | 0/0 | 2/0 | 0/2 | 0/0 |
T _{6} | 0/0 | 0/0 | 0/2 | 1/0 | 0/0 | 0/3 |
T _{7} | 0/1 | 0/0 | 0/2 | 1/0 | 2/0 | 0/0 |
T _{8} | 1/0 | 0/0 | 0/2 | 0/0 | 0/0 | 3/0 |
In Table 3, each resource is held or requested by some tasks. For example, R_{1} is held by T_{1} and T_{1} and T_{8}. The number of R_{1} held by T_{1} and T_{1} and T_{8} is 2, 1, and 1, respectively. R_{1} is also requested by T_{7}, and the number of R_{1} requested by T_{1} is 1.
Seven strategies for deadlock solving
Main parameter | Value |
---|---|
Initial population size M | 20 |
Crossover probability P_{ c } | 0.5 |
Mutation probability P_{ m } | 0.1 |
Iterations | 100 |
5.2 Experimental result and analysis
From the experimental results, we can see that employing the genetic algorithm to solve this deadlock problem needs only six iterations, and the least cost of the optimal deadlock solving strategy is 22, which demonstrates the greatly efficiency of genetic algorithm.
In following experiments, we will investigate the impact of parameters on our method performance, including M, P_{ c }, and P_{ m }.
5.3 Impact of M
From Figure 8, we can see that when the population size increases, the number of evolution iteration declines, which means that the global search ability enhances and the convergence time will fall. Particularly, these changes are not monotonic increase or decrease, but with the increase of population size, change is fluctuations or shocks.The smaller the population size, the more obvious volatility.
5.4 Impact of P_{ c }and P_{ m }
Figures 9 and 10 illustrate the impact of P_{ c } and P_{ m } on best fitness of our model, respectively. We can see that: 1) The better P_{ c } is 0.5 when P_{ c } is set as 0.1, 0.5, and 0.9, while the better P_{ m } is 0.1 when P_{ m } is set as 0.1, 0.6, and 0.9. 2) If P_{ c } is too small (e.g. 0.1, etc) or too large (e.g. 0.9, etc), the algorithm cannot guarantee convergence in the global optimal solution. 3) The greater the P_{ m }, the more unstable the fitness value. If P_{ m } is too small (e.g. 0.06, etc), the algorithm will drop into the situation of local optimum. If P_{ m } is too large (e.g. 0.9, etc), the global search ability is also weakened. 4) Small cross rate or mutate rate will increase the iteration number of times and large cross rate or mutate rate will accelerate the calculation, but there is no guarantee that they will converge to global optimal solution.
6 Conclusion and future work
In this paper, we investigate the problem of deadlock in vehicle sensing systems in Internet of Vehicles. This paper proposes an efficient deadlock solving method for vehicle sensing systems. In this method, the deadlock problem is analyzed based on four deadlock occurred conditions. Tasks and resource allocation characteristics are described by mathematical expressions. Combining the quantity and cost of tasks, a deadlock solving optimization model is developed for vehicle sensing systems. To quickly solve the deadlock and to ensure the minimum cost, co-evolutionary genetic algorithm (CGA) is used to search for optimal deadlock solving strategies. Simulations are conducted and the experimental results show the efficiency of the proposed deadlock solving method based on CGA.
There are several potential future directions for our method. First, we will investigate more sophisticated ways to combine more factors (e.g. urgency degree, priority, etc.) to improve the optimization model. Second, we will propose more efficient and effective deadlock solving methods. In addition, we will also conduct more comprehensive and realistic experiments in real physical systems to evaluate our approach.
Declarations
Acknowledgments
The work described in this paper was supported by the National Natural Science Foundation of China (Grant No. 61472338 and 61332010) and the Research Grants Council of the Hong Kong Special Administrative Region, China (Grant No. CUHK 415212 of the General Research Fund).
Authors’ Affiliations
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