Exponential replicate broadcasting mechanism for adaptive bandwidth allocation in mobile networks
- Chi-Kun Lin^{1},
- Gwo-Jiun Horng^{2}Email author,
- Chi-Hsuan Wang^{1} and
- Jar-Ferr Yang^{1}
https://doi.org/10.1186/1687-1499-2014-136
© Lin et al.; licensee Springer. 2014
Received: 28 February 2014
Accepted: 6 June 2014
Published: 18 August 2014
Abstract
This paper proposes an exponential replicate broadcasting (ERB) algorithm for data dissemination to improve data access efficiency. The proposed ERB algorithm first constructs a broadcast tree to determine the broadcast frequency of each data and splits the broadcast tree into some broadcast wood to generate the broadcast program. In addition, this paper develops an analytical model to derive the mean access latency of the generated broadcast program. In light of the derived results, both the index channel's bandwidth and the data channel's bandwidth can be optimally allocated to maximize bandwidth utilization. This paper presents experiments to help evaluate the effectiveness of the proposed strategy. From the experimental results, it can be seen that the proposed mechanism is feasible in practice.
Keywords
ERB algorithm Skewed broadcast Indexing technique Access time Tuning time Bandwidth allocation1 Introduction
Mobile web services are a new generation of web services accessible to mobile clients through the air in support of anytime-and-anywhere access to services [1, 2]. Furthermore, owing to the characteristics of wireless environments including device mobility, scarce bandwidth, and limited battery power, accessing services in wireless-oriented service environments has become an emerging challenge to the data-management and telecommunication communities [3].
In essence, there are two fundamental modes for data service dissemination in a wireless region: the broadcasting mode and the on-demand mode [4]. In a broadcasting mode, data is broadcast periodically to mobile devices according to a broadcast program in the region [5–14]. To fetch a data record, mobile clients have to wait until the target data appears on the broadcast channel. In this way, a broadcast-based system can serve thousands of mobile users simultaneously, since the broadcast cost is identical regardless of the number of users. The other data dissemination mode is the on-demand mode. This mode is similar to the traditional client-server approach. In the on-demand mode, a mobile node first sends its query on an uplink channel and the server sends the requested data to the client through the downlink channel. In this paper, we consider data disseminated in a broadcast-based wireless environment.
In the literature, access efficiency and energy consumption are two issues of concern in assessing the performance of wireless communication systems [4, 15]. Access efficiency can be evaluated by access time, which means the time that has elapsed from the moment a client requests data to the moment the client retrieves the target item. Energy consumption concerns the battery power consumed by the client to retrieve the requested data, and it can be quantified according to tune-in time [15]; in other words, according to the amount of time the mobile device stays active ‘listening’ to the broadcast channel.
The plain broadcast scheme is the simplest approach to generating data broadcast programs and has been adopted in earlier research [3, 16]. Using this approach, the server broadcasts all data records in a round robin manner. Therefore, this method is easily implemented. Furthermore, since the plain broadcast scheme treats all data items equally, the average waiting time for each packet of data equals half of the overall broadcast period. As a result, it is clear that this scheme is not feasible for cases in which data access frequencies are not uniform.
An alternative data dissemination mechanism is the broadcast disks scheme, which permits data items to be broadcast with different frequencies [5]. This algorithm first divides data items into a few groups (i.e., disks) such that data items with similar popularity are assigned to the same disks. Afterwards, it determines the rotation speed of each disk according to the popularity of data items. In this way, one can construct a broadcast program that adjusts the trade-off between the access time of hot data and that of cold data.
In addition to access efficiency, power conservation is critical for mobile nodes owing to limited battery capacities [17–19]. To facilitate power saving, it is necessary for mobile devices to support two operation modes: the active mode and the doze mode [20]. Mobile clients normally operate in the active mode, and they can switch to the energy-saving doze mode when mobile devices become idle. Thus, keeping mobile devices in the doze mode for as long as possible could be achieved through the application of an air index technique.
By broadcasting the arrival time of data items to clients, mobile devices can stay in the doze mode until the requested data arrives. In this way, the tune-in time can be reduced to the initial index probe time plus the data-retrieval time. At present, several research efforts have addressed reducing the initial probe time [15, 21–26]. These studies complement our work in different aspects.
In this paper, we investigate the effects of data-access frequency and data size on access efficiency. And we propose the ERB algorithm for skewed data access to generate an efficient broadcast cycle. The ERB algorithm first generates a broadcast tree to determine the broadcast frequency of each data record. After that, the broadcast tree is split into broadcast wood to balance the inter-broadcast time of successive copies of data. In order to reduce the tune-in time, we further separate one individual channel from the broadcast channel to broadcast index packets.
The rest of this paper is organized as follows: Section 2 introduces related data-broadcast research. The system architecture used throughout this paper is presented as well. Section 3 discusses the proposed ERB algorithm and its role in improving data access latency. Section 4 establishes an analytical model for optimizing index-channel and data-channel bandwidth allocation. Section 5 discusses the proposed dynamic broadcast adaptive for weight change. Section 6 discusses experiments serving to evaluate the performance of the proposed mechanism. Finally, section 7 remarks on the conclusions drawn.
2 Related work
This section reviews important attempts at applying data broadcasting, bandwidth allocation in mobile networks. This paper develops an analytical model to approximate the proposed ERB algorithm. This model makes it convenient to efficiently evaluate the mean access time of the generated broadcast program. Moreover, in light of the derived access time, both the index channel's optimum bandwidth allocation and the data channel's optimum bandwidth allocation are formulated.
In order to assess the feasibility and efficiency of our mechanism, we conducted several experiments. Results reveal that the proposed ERB algorithm performs well in terms of data-access efficiency. Moreover, the optimum bandwidth allocation yields a significant performance improvement in tune-in time. As a consequence, it can be seen from experimental results that putting the proposed mechanism into practice is entirely feasible.
In [5], this scheme assumes that data items are of equal sizes. In terms of practicality, it is not efficient to apply the broadcast disks to the varied-size data items. Moreover, it is hard for system developers to define the similarity of data popularity so as to partition data items into disks. The determination of the relative broadcast frequency for each disk is also imprecise. In this paper, we propose a ERB algorithm for varied-size data items to tackle the above drawbacks. The ERB algorithm grows a broadcast tree to determine the broadcast frequency of each data record. After that, we split the broadcast tree into some broadcast wood with similar sizes so as to place those data items in the broadcast cycle. The details are described clearly in section 3.
On the other hand, when a user submits queries to a mobile client, the mobile equipment first fetches an index packet from the index channel to get the arrival time of the target data. Then the mobile device switches from the active mode to the doze mode for energy savings until the target item appears on the data channel [20]. After that, the mobile client downloads the target data transaction so as to process the user's request.
Data structure in servers’ databases
Data identifier | Access probability | Size |
---|---|---|
D _{ 1 } | Pr(D_{ 1 }) | S(D_{ 1 }) |
D _{ 2 } | Pr(D_{ 2 }) | S(D_{ 2 }) |
D _{ 3 } | Pr(D_{ 3 }) | S(D_{ 3 }) |
… | … | … |
3 Exponential replicate broadcasting
In this section, we propose the ERB algorithm as a way to improve the performance of existing data-broadcasting mechanisms. According to the statistical probability of data access, the ERB algorithm broadcasts a significant number of copies for popular data in a broadcast cycle to diminish the average access time. In addition, the proposed algorithm balances the inter-broadcast time of successive copies of a single packet of data even though data-item sizes can vary.
Notation used in the ERB mechanism
Notation | Definition |
---|---|
N | The number of data items |
Pr(D _{ i } ) | The access probability of data item D_{ i } |
S(D _{ i } ) | The size of data item D_{ i } |
n _{ i } | The broadcast frequency of data item D_{ i } |
ℜ | Sapling |
ℑ | Broadcast tree |
h | Tree height |
L | The length of a broadcast cycle |
B | The bandwidth of a broadcast channel |
τ | Maximum tree height of a broadcast tree |
w _{ i } | The i-th broadcast wood |
3.1 Data-item reordering
The first step of the ERB algorithm is to sort all data records in the database by their access frequency and size. More precisely, after performing the data-item reordering, we would get a broadcast cycle [D_{1}, D_{2}, …, D_{ N }] such that (1) Pr(D_{ i }) ≥ Pr(D_{ j }) and (2) if Pr(D_{ i }) = Pr(D_{ j }), then S(D_{ i }) ≤ S(D_{ j }) for any integers i < j ≤ N. To reduce the average access time, it is beneficial to broadcast hotter data more frequently [27, 28]. Therefore, sorting data records from hottest to coldest can make it convenient to determine the broadcast frequency of each data item.
3.2 Broadcast-tree construction
Once the broadcast tree is built, the broadcast frequency n_{ i } for each data item D_{ i } is determined as well. The number of replicates in the broadcast tree stands for the data's broadcast frequency. Thus, take the broadcast tree in Figure 3 as an example. In this case, we have n_{1} = 4, n_{2} = n_{3} = 2, and n_{4} = n_{5} = … = n_{11} = 1. Specifically, the criterion for estimating which data items should be moved to the next level is determined by the following theorem.
According to theorem 1, the constancy of bandwidth B facilitates the broadcast tree construction procedure. The broadcast tree construction starts with the sorted data elements from the data item reordering.
Afterward, we use theorem 1 to determine the optimal cutpoint c for each level and move data records D_{1}, …, D_{ c } to the next floor so as to reduce the overall access time. In addition, note that the maximum height of the generated broadcast tree is limited by parameter τ. This factor can prevent the following procedures from taking too much execution time.
Broadcast-tree construction procedure
0. Initial settings: Let h = 0, m = N, $\mathit{L}={\displaystyle \sum _{\mathit{i}=1}^{\mathit{N}}\mathit{S}\left({\mathit{D}}_{\mathit{i}}\right)}$ and n_{ i } = 1 for i = 1, 2, …, N
1. Let $\mathit{F}\left(\mathit{r}\right)=\frac{\mathit{L}}{{2}^{\mathit{h}+2}}{\displaystyle \sum _{\mathit{i}=1}^{\mathit{r}}Pr\left({\mathit{D}}_{\mathit{i}}\right)}-\left({\displaystyle \sum _{\mathit{i}=1}^{\mathit{r}}\frac{Pr\left({\mathit{D}}_{\mathit{i}}\right)}{4}}+{2}^{\mathit{h}-1}{\displaystyle \sum _{\mathit{i}=\mathit{r}+1}^{\mathit{N}}\frac{Pr\left({\mathit{D}}_{\mathit{i}}\right)}{{\mathit{n}}_{\mathit{i}}}}\right)\phantom{\rule{2pt}{0ex}}\left({\displaystyle \sum _{\mathit{i}=1}^{\mathit{r}}\mathit{S}\left({\mathit{D}}_{\mathit{i}}\right)}\right)$
Find the cutpoint c such that $\mathit{F}\left(\mathit{c}\right)=\underset{1\le \mathit{r}\le \mathit{m}}{max}\left\{\mathit{F}\left(\mathit{r}\right)\right\}$.
2. If F(c) ≤ 0 or h > τ, then return the expanded full binary tree ℑ.
3. else move the data items {D_{1}, …, D_{c}} to the next level.
4. Set h = h + 1, m = c, $\mathit{L}=\mathit{L}+{\displaystyle \sum _{\mathit{i}=1}^{\mathit{c}}{\mathit{n}}_{\mathit{i}}\mathit{S}\left({\mathit{D}}_{\mathit{i}}\right)}$, and n_{ i } = 2^{ h } for i = 1, 2, …, c. Then go to step 1.
3.3 Wood-size equalization
Once the number of duplicates for each data item is obtained, we determine the replicated data placement in the broadcast cycle. Clearly, to achieve a better performance, the inter-broadcast time of successive copies of data should be the same. However, it is known that such an optimum placement problem associated with the variant data sizes is an NP-complete problem [29]. Consequently, in this subsection, we develop a wood-size equalization algorithm to place those data items in the broadcast cycle.
The functionality of the wood-size equalization is to split broadcast tree ℑ of height h into 2^{ h } pieces of broadcast wood (w_{1}, …, ${\mathit{w}}_{{2}^{\mathit{h}}}$) with similar sizes. Actually, the wood size equalization is a recurrence in structure. It splits the broadcast tree in a bottom-up manner. Given broadcast tree ℑ, we first get 2^{ h }/2 broadcast woods by applying the wood size equalization to the left subtree of ℑ and get the other 2^{ h }/2 broadcast woods from the right subtree of ℑ. Afterward, the Root-cutting procedure permits the distribution of the the data in the root to these woods such that each broadcast wood has a similar size. The wood-size equalization can be stated as follows.
Basically, the root-cutting procedure adopts a greedy strategy to divide the root node. More precisely, the root-cutting procedure iteratively splits the data a_{ i } with the largest data size from the root and attaches it to the minimum-sized wood w_{ c } until all the data in the root are allocated. Thus, we can summarize the root-cutting procedure in the following algorithmic form.
Wood size equalization (WSE, ℑ)
// Let h: = the height of broadcast tree ℑ,
// R: = the root node of tree ℑ
// ℑ_{ L } andℑ_{ R } denote the left and the right subtrees of ℑ.
1. If h = 0, then return ℑ.
2. else (w_{1}, …, ${\mathit{w}}_{{2}^{\mathit{h}-1}}$) := WSE(ℑ_{ L }),
3. (${\mathit{w}}_{{2}^{\mathit{h}-1}+1}$, …, ${\mathit{w}}_{{2}^{\mathit{h}}}$) := WSE(ℑ_{ R }),
4. return Root-Cutting(R, β_{1}, …, ${\mathit{\beta}}_{{2}^{\mathit{h}}}$).
Root-cutting procedure (R, w _{ 1 } , …, w _{ v } )
1. Sort the data items in root R according to their sizes into a non-increasing order a_{1}, …, a_{k.}
(i.e., S(a_{ i }) ≥ S(a_{ j }) iff 1 ≤ i ≤ j ≤ k).
2. For i = 1 to k
3. Find the wood w_{ c } with the smallest size.
4. Split the data item a_{ i } from the root and attach it to the top of wood w_{ c }.
3.4 Complexity analysis
In this subsection, the time complexity of the ERB algorithm is studied. Recall that the ERB algorithm contains three steps. The first step is the data-item reordering, which requires time complexity O(N logN) for sorting N data items. In addition, the second step, broadcast-tree construction, builds a broadcast tree having a height of at most τ . For each level, it takes at most O(N) time to determine the fittest cutpoint. And then, it requires O(2τN) time to expand from a sapling to a full binary tree. Finally, the wood size equalization is a recurrence in structure, and it requires a time complexity of O(τ N logN + τ 2^{ τ }N). Besides, because the value τ is relatively insignificant for the large value of N, we concluded that the proposed ERB algorithm takes only a time complexity of O(N logN) in total.
4 Optimum bandwidth allocation
In this section, we develop an analytical model to approximate the average access time of the proposed ERB algorithm (refer to theorem 3). Afterward, this analytical model helps derive the optimum bandwidth allocation for our system architecture and minimize the average access time (refer to theorem 4). The details are described as follows.
On the other hand, since each data item D_{ i } has its own access probability Pr(D_{ i }), the average data access time can be expressed as a weighted summation of the average access time of all data items. In terms of mathematic form, the mean data access time can be formulated as follows:
where T _{access} (D _{ i } ) stands for the random variable representing the access time of the specific data item D _{ i } .
On the other hand, it is not easy to derive the waiting time T_{wait}(D_{ i }) directly for some data items D_{ i } because the proposed ERB algorithm broadcasts duplicates for those data items with high-access probability. Furthermore, the positions of the replicated data items in the broadcast cycle also determine the data items' waiting time. Therefore, in this paper, we introduce a specific L_{ i }[k]-function to represent the position of the k th replicate of the data item D_{ i } in the broadcast cycle.
Definition 1 The length L of a broadcast cycle is defined as the total number of data bits in this broadcast cycle. And the term L_{ i }[k] is defined as the total number of broadcasted bits before broadcasting the k-th replicate of the data item D_{ i } in a broadcast cycle.
Example Take the broadcast cycle depicted in Figure 6 as an example. In this case, we have the equations L_{1}[1] = S(D_{11}) + S(D_{4}) + S(D_{3}), L_{1}[2] = L_{1}[1] + S(D_{1}) + S(D_{8}) + S(D_{2}), L_{1}[3] = L_{1}[2] + S(D_{1}) + S(D_{5}) + S(D_{10}) + S(D_{7}) + S(D_{3}), and L_{1}[4] = L_{1}[3] + S(D_{1}) + S(D_{9}) + S(D_{6}) + S(D_{2}). The length of the broadcast cycle L is equal to L_{1}[4] + S(D_{1}).
Proof Without loss of generality, we assume that the mobile client starts to wait for the target data item D_{ i } in the m-th broadcast cycle as in Figure 10. Since the data D_{ i } is broadcast n_{ i } times during a broadcast cycle, the mobile client retrieves the nearest replicate of the target D_{ i } according to the entry time the mobile client starts to wait. So with time T being the time at which the mobile device starts to wait, we now consider three cases for calculating the waiting time.
Case 1: $0\le \mathit{T}<\frac{{\mathit{L}}_{\mathit{i}}\left[1\right]}{{\mathit{B}}_{\mathrm{D}}}$
Case 2: $\frac{{\mathit{L}}_{\mathit{i}}\left[\mathit{k}\right]}{{\mathit{B}}_{\mathrm{D}}}\le \mathit{T}<\frac{{\mathit{L}}_{\mathit{i}}\left[\mathit{k}+1\right]}{{\mathit{B}}_{\mathrm{D}}},\mathit{k}=\phantom{\rule{0.25em}{0ex}}1,\phantom{\rule{0.25em}{0ex}}2,\phantom{\rule{0.25em}{0ex}}\dots ,{\mathit{n}}_{\mathit{i}}-1.$
Case 3: $\frac{{\mathit{L}}_{\mathit{i}}\left[{\mathit{n}}_{\mathit{i}}\right]}{{\mathit{B}}_{\mathrm{D}}}\le \mathit{T}<\frac{\mathit{L}}{{\mathit{B}}_{\mathrm{D}}}$
According to the above lemmas and theorems, the average access time can be computed as well. We now summarize the derivation of the average access time via the following theorem.
From theorem 3, we not only can estimate the average access time of a query for any broadcast program, but also determine the optimum bandwidth allocation for both the index channel and the data channel. Consider the case in which the overall channel bandwidth for data broadcasting is B. Then for the proposed ERB algorithm to achieve the minimum access time, the optimum bandwidth allocation is given by the following theorem.
where $\mathit{\xi}={\displaystyle \sum _{\mathit{i}=1}^{\mathit{N}}Pr\left({\mathit{D}}_{\mathit{i}}\right)\xb7\left\{\frac{1}{2\mathit{L}}\xb7\left[\begin{array}{l}{\left(\mathit{L}-{\mathit{L}}_{\mathit{i}}\left[{\mathit{n}}_{\mathit{i}}\right]+{\mathit{L}}_{\mathit{i}}\left[1\right]\right)}^{2}\\ +{\displaystyle \sum _{\mathit{k}=1}^{{\mathit{n}}_{\mathit{i}}-1}{\left({\mathit{L}}_{\mathit{i}}\left[\mathit{k}+1\right]-{\mathit{L}}_{\mathit{i}}\left[\mathit{k}\right]\right)}^{2}}\end{array}\right]+\mathit{S}\left({\mathit{D}}_{\mathit{i}}\right)\right\}}$
As a consequence, to minimize the average access time E[T_{access}], we need to choose the values B_{ I } and B_{D} to minimize the estimation function Φ(B_{ I }, B_{D}) subject to the constraint B_{ I } + B_{D} = B.
5 Performance evaluation
5.1 Simulation Environment
Parameter setting
Parameters | Values |
---|---|
The number of data items (N) | 50 ~ 100 |
Bandwidth (B) | 80 KB/s |
Index packet size (S_{ index }) | 128 bytes |
The sizes of data items (S(D_{ i })) | Normal distribution |
(mean 50 ~ 150 KB, variance 900 KB^{2}) | |
The access probabilities (Pr(D_{ i })) | Zipf distribution |
(θ = 0.5 ~ 1.5) | |
The number of requests | 5,000 |
Maximum height of broadcast the tree | 3 |
where the value of skew factor θ ranged from 0.5 to 1.5.
We did not employ any indexing technology for the index channel in our system, and in this way, we could realize the actual effects of the proposed method. The index packet size was assumed to be 128 bytes. Further, the total available bandwidth including the index and data channel was set to 80 KB/s [3].
In addition to the proposed system architecture, we implemented a plain broadcast, broadcast disks, and an exponential index scheme for comparison. In line with the simulation model in [1], the broadcast disk technology was implemented with three broadcast disks. And the relative frequencies between these disks were dominated by parameter Δ. More precisely, the broadcast frequency of the disk i was determined by ref_freq(i) = (3 − i) Δ + 1. In the experiments, we considered three kinds of broadcast disk schemes: Δ = 1, 2, and 3.
For each experiment, we generated five different datasets, each one containing 50 ~ 100 data records for broadcasting. In addition, for each data set, we generated 5,000 queries and calculated the corresponding access time and tune-in time to evaluate access efficiency and power conservation. The inter-arrival time of queries followed an exponential distribution with an arrival rate of λ = 1. The simulator and query generator were coded in MATLAB.
5.2 Experimental results
This work applies average tune-in time and average access time as the performance measurement. It allows devices to power on when they need to access data so that these two values are lower than the ones gained by other method. It means that this device can stay in sleep mode longer and save more energy.
In conclusion, even though our system releases some bandwidth to broadcast index packets, our experimental results show that our mechanism exhibits better access latency than the plain broadcast and broadcast disks scheme do, especially when the data access probabilities are skewed. In terms of power conservation, it is clear that our system can reduce much more tune-in time if a proper indexing technology is applied to our index channel. In addition, the numerical results of our experiments confirm the accuracy of the proposed approximation model.
6 Dynamic broadcast adaptive for weight change
The proposed TAB algorithm first constructs a broadcast tree to determine the broadcast frequency of each data, and splits the broadcast tree into some broadcast wood to generate the broadcast program. In addition, this paper develops an analytical model to derive the mean access latency of the generated broadcast program. In light of the derived result, the bandwidth for both index channel and data channel can be optimally allocated to maximize bandwidth utilization. In this section, we improved the dynamic broadcast adaptive method, not only to solve but also for high efficiency and performance of the facture environment.
We found that the proposed algorithm is presently in the broadcast structure. The wireless broadcast scheduling has been considered the data item frequency of the fixed and it has an unreasonable supposition. The data item frequency would be the request of the client for a change under the factual dynamic environments. Each of the data item has a frequency value itself and the each frequency of data item should been computed for its weight value and adjusted for dynamic broadcast adaptive so the frequency of data item has no fixed probability value.
6.1 Players of data item
A set of agents who play the game, N_{D} = {1, …, n }, with typical element i ∈ N_{D}. In the strategies, for each i ∈ N_{D} there is a nonempty set of strategies S_{ i } with typical element s_{ i } ∈ S_{ i }, S_{ i } = {(D_{i − 1}), (D_{i + 1}) }. For weight function u_{ i }, S ↦ ℜ assigned to each players of data item i, where strategies profile is equal to s ∈ S = × _{i ∈ N}S_{ i }. It can be written as a normal form game G = 〈N, { S_{ i } }_{i ∈ N} , { u_{ i } }_{i ∈ N}〉.
6.2 Best response functions for interaction
Nash equilibrium is a strategy profile where every data weight is underlined. This suggests an alternative definition for Nash equilibrium involving best-response functions. The best-response function for data item i ∈ N is set-weight valued function B_{ i } such that ${\mathit{B}}_{\mathit{i}}\left({\mathit{s}}_{-\mathit{i}}\right)=\phantom{\rule{0.25em}{0ex}}\left\{{\mathit{s}}_{\mathit{i}}\in {\mathit{S}}_{\mathit{i}}|{\mathit{u}}_{\mathit{i}}\left({\mathit{s}}_{\mathit{i}},{\mathit{s}}_{-\mathit{i}}\right)\ge {\mathit{u}}_{\mathit{i}}\left({\mathit{s}}_{\mathit{i}}^{\prime},{\mathit{s}}_{-\mathit{i}}\right),\forall {\mathit{s}}_{\mathit{i}}^{\prime}\in {\mathit{S}}_{\mathit{i}}\phantom{\rule{0.25em}{0ex}}\right\}$. So that B_{ i }(s_{− i}) ⊆ S_{ i } ‘tells’ data item i what to do when the other data items play s_{− i}.
7 Conclusion
Data broadcasting involves important data dissemination technology for accessing mobile services in wireless networks. In general, there are two main approaches to data broadcast, viz., push-based broadcast and on-demand broadcast [32]. Mobile internet and mobile services that make use of mobile data are increasingly popular [33–43]. Among others, access efficiency and power conservation are two critical performance indexes for assessing the effectiveness of wireless communication systems. In this paper, we present an ERB algorithm to reduce the response time of mobile clients' requests. We provide an analytical model to measure the expected access latency of the generated broadcast program. This analytical model helps formulate the optimum bandwidth allocation for index and data channels. From the experimental results, it can be seen that our mechanism outperforms the existing data broadcast schemes in terms of access time. Moreover, the optimum bandwidth allocation also brings a significant improvement in energy conservation. Based on these advantages, it can be seen that the proposed mechanism is scalable and can feasibly increase the efficiency of data dissemination in broadcast-based systems.
Declarations
Acknowledgement
We thank the National Science Council of Taiwan for funding this research (Project no.: NSC 102-2218-E-268-001).
Authors’ Affiliations
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